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Engineering Trainee-Teachers’ Attitudes Toward Technology Use in Pedagogical Practices: Extending Computer Attitude Scale (CAS)

Engineering Trainee-Teachers’ Attitudes Toward Technology Use in Pedagogical Practices: Extending... The current study examined the trainee teachers’ attitudes toward technology adoption and use in tertiary engineering education. The Computer Attitude Scale (CAS) was extended by including the social influence component, to examine whether social norms affect the acceptance of technology by teachers. Findings from 110 trainee-teachers revealed that their attitudes toward technology are positive. These attitudes constitute the way they like and intend to use technology, their perception of its usefulness in their daily tasks, and the control they perceived to have over technology while using it in engineering disciplines. The findings also confirm that social influence is an important predictor of trainee teachers’ attitudes toward using technology. Overall, the study provides a new influential factor (social) that could be merged with the other four major components (affect, perceived usefulness, perceived control, and behavioral intention) of CAS in conducting future research. The results of this study further provide useful knowledge that extends prior arguments concerning teachers’ attitudes toward using technology in teaching with respect to age, gender, and disciplines. More specifically, the study, theoretically, contributes to research practice in technology acceptance, by extending the computer attitude scale (CAS), with social influence as an additional important factor to be considered when conducting future research. Therefore, an extended CAS is established for exploring newer research in this domain. Policymakers and designers of teacher professional development will be informed of these findings that will accelerate initiatives of technology integration of engineering education in developing countries and other similar contexts. Keywords engineering education, technology use, teachers’, attitudes, trainee-teacher, Computer Attitude Scale and adopted (Almerich et al., 2016; Ifinedo et al., 2020). For Introduction example, Yarbro et al (2016) opined that as educators use The dynamics of today’s evolving technology are influenc- technology to improve students learning, the most important ing nearly every aspect of human life and are still changing role in technology integration is providing a learning envi- the way we do almost everything. The increasing involve- ronment that will support learners with active, hands-on, and ment of human-technology interaction in everyday tasks has authentic learning activities for offering enhanced myriad led to occupational and personal success (Bonina et al., 2021; learning experiences. Ultimately, teachers play a key role in Papanastasiou et al., 2019; Reis et al., 2018; Yildiz Durak, arranging technology-enhanced learning spaces (S. H. Khan, 2021). For students of today’s digital age to succeed in their 2015). Teachers, in a broader perspective, play a pivotal quest for knowledge and skills, there is a need for educa- role in realizing successful changes at all levels of education tional planners, policymakers, and practitioners to embrace (Van der Heijden et al., 2015). In relation to technology modern technology (Uerz et al., 2018). It is also imperative that classroom experiences be adequately equipped to pro- Islamic University of Technology, Gazipur, Dhaka, Bangladesh vide equitable and unbiased access to technological tools for Corresponding Author: students regardless of gender or ethnicity (Sahin et al., 2016; Md. Shahadat Hossain Khan, Department of Technical and Vocational Teo, 2008). Education, Islamic University of Technology, Room: 302, Academic In doing so, teachers are the motivational force through Building 1, Board Bazar, Gazipur, Dhaka 1704, Bangladesh. which technological tools can be introduced, implemented, Email: skha8285@iut-dhaka.edu Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open integration, a significant number of studies had explored the Related Literature positive impact of technology in the learning environment. Engineering Education and the Evolving Some of these include the research work of M. S. H. Khan et al., (2019) on the use of mobile devices in higher educa- Pedagogical Needs tion in Bangladesh, which yielded five ways of using mobile Engineering education consists of a synthesis of core math- devices in their learning that could have a positive impact on ematics and scientific principles, design concepts, and cut- student learning. Henderson et al (2017) conducted a study ting-edge technologies relevant to industry and research on how university students perceive the usefulness of digital jobs. It comprises the development of skills that will persist technologies in their learning. Their results revealed a wide for more than a few years after graduation and will serve as range of distinct digital benefits; such as flexibility of time, the basis for lifelong learning, enabling graduates to have a ability to communicate and collaborate at different places significant impact on society beyond graduation and for and locations, as well as retrieving, reviewing, and research- many years to come (Schor et al., 2021). It is also about mas- ing information. However, regardless of evidence that shows tering a variety of critical skills for managing cross-disci- the positive impacts of technology on educational practice, plinary projects. Engineering faculties all over the world are teachers’ resistance to its application at the instructional level frequently revising and modifying their curricula in response still exists in many cases (Seufert et al., 2021). to evolving trends, industry feedback, accreditation organi- Effective pedagogical use of technology among teachers zations, and a variety of other factors to ascertain that their remained negligible in Bangladesh where this research was graduates are equipped for an ever-changing world (Schor conducted (Fahadi & Khan, 2022). The government of et al., 2021). Bangladesh however, has shown its political will through Engineering education is crucial for humanitarian, social, various intervention programs aimed at promoting access to and economic development, and its graduates must be pre- technology, and its acceptance among teachers and students pared to address the everchanging sustainability concerns. (Obaydullah & Rahim, 2019). As part of the effort to The fourth industrial revolution, which is currently on the strengthen gender equity in digital literacy, the government political and industrial agenda, involving a widespread inte- made ICT a mandatory subject for all students from K6 gration of technologies such as automation, the Internet of through K12 and established a program that will ensure Things (IoT), artificial intelligence (AI), robotics, neuro- e-learning capacity among all female teachers by the year technologies, and virtual and augmented realities, is a more 2023 (Z. Hossain et al., 2019). However, having a technology- recent challenge (Hadgraft & Kolmos, 2020). A combination enabled learning environment, as well as training teachers, of these technologies intertwin in applications to transform does not guarantee effective pedagogical integration unless industrial processes into systems that are more connected, the teachers’ technological perceptions and attitudes are pos- reliable, predictable, and robust, with a high degree of cer- itive (Farjon et al., 2019; Khan & Hasan, 2013). Even though tainty (Gupta, 2020). technology integration is guided by government policies, As a result, “engineering education is experiencing a teachers still hold the autonomy to decide when and how to paradigm shift from teacher-centric to the student-centric use it (Teo, 2011). It, therefore, becomes imperative to have teaching-learning process, content-based education to out- an insight into the teachers’ attitudes toward technology, so come-based education, knowledge-seeking to knowledge that progress related to possible successful technology inte- sharing classrooms, teachers to facilitators, traditional engi- gration can be obtained. Moreover, true insight can best be neering disciplines to interdisciplinary courses, chalk and sought from trainee-teachers, who are in the making, and board (lecture-based) learning to technology-driven learn- whose attitudes are less likely to be swayed by the old peda- ing” (Pavai Madheswari & Uma Mageswari, 2020, p. 01). gogical trends. This paradigm shift results in a new era of engineering edu- There is a global outcry for the need to support skill- cation known as “Engineering Education 4.0” (Frerich et al., building for students in science technology engineering 2016). Engineering Education 4.0 involves the implementa- and mathematics (STEM) to enable them to thrive in tion of current and emerging technologies combined with today’s technology-driven era (Rifandi et al., 2019). This innovative pedagogical approaches inspired by the fourth has given birth to the urgent need to investigate the techno- industrial revolution (IR4.0) for flawlessness, satisfaction, logical attitudes of trainee-teachers in this segment of edu- time-saving, skill development, and efficiency enhancement cation, particularly engineering education trainee-teachers. in engineering education (Gupta, 2020). While acknowledg- This study focuses on the attitudes toward the technology ing the fact that the students of today’s engineering education of engineering trainee-teachers in a developing country. are born in the digital age, and are normally referred to as This research may be crucial for further improvements in generation Z (Opri & Ionescu, 2020), which makes them engineering education teacher professional development capable of withstanding technological challenges posed by programs (ETPD), in Bangladesh and other countries with the evolving fourth industrial revolution (IR 4.0), there are similar contexts. various factors outside the control of the students, such as Salele and Khan 3 digital divide issues related to access to technology and more ICT tools by teachers based on their gender or educational importantly conservative pedagogical approaches adopted qualifications. The only variation identified between lectur- by instructors (Gonçalves & Capucha, 2020; Salem & ers was attributed to their competence, which is normally Mohammadzadeh, 2018), the latter of which has become a accumulated over time, thus implying the impact of years of major concern in technology integration studies (Arkorful technology use on its efficient use. Recently, Shodipe and et al., 2021; Pamuk, 2022; Papadakis, 2018; Raman et al., Ohanu (2021), surveyed 418 electrical and electronic tech- 2015; Sánchez-Prieto et al., 2019; Shah et al., 2020; Shodipe nology teachers in higher education about their attitudes, & Ohanu, 2021; Wijnen et al., 2021; M. Xu et al., 2021; S. engagement, and disposition toward mobile learning. They Xu & Zhu, 2020; Yildiz Durak, 2021). It is therefore impor- discovered a positive correlation between teachers’ per- tant than ever, for engineering education teachers and instruc- ceived ease of use and actual use of mobile learning, as well tors to embrace and use the evolving technology-driven as a positive correlation between teachers’ disposition and pedagogical approaches, with the goal of improving stu- perceived ease of use, which forms the foundation of human dents’ engagement and content delivery, so that students can attitude and behavior (Shodipe & Ohanu, 2021). In addition, achieve the feat essential for evolving engineering practice. Saefuddin et al. (2019), reported an overall positive attitude toward technology by science teachers surveyed in the south- east province of Indonesia. However, the study identified a Teachers’ Attitudes Toward Technology Use small percentage of the teachers who do not agree that tech- The attitudes and beliefs of teachers toward technology are nology is an efficient communication and presentation tool crucial for school leaders to understand as schools shift for effective teaching and learning activities. S. Xu and Zhu toward modern digital pedagogy (Mou, 2016; Shah et al., (2020), identified key factors that affect teachers’ acceptance 2020). Subdomains of attitudes toward technology include and integration of technology as attitude and technology perceived usefulness, control, liking behavioral intention, beliefs, and self-efficacy with respect to technology use. and confidence (Mahajan, 2016). Other factors include age, They also identified a positive correlation between attitude gender (Hrtovnová et al., 2015; Teo, 2014), technology con- and technology belief and intention to integrate technology fidence (Miller et al., 2017), anxiety (Chiu & Churchill, in teaching. In research conducted by Farjon et al. (2019), on 2016), and self-efficacy (Brantley, 2018). Baturay et al. the technology integration of pre-service teachers, it was (2017), in a study on “the relationship among pre-service revealed that although access to technology has an impact on teachers’ computer competence, attitude toward computer- pre-service teachers’ technology integration, their attitudes assisted education, and intention of technology acceptance,” and views about technology integration remain a major fac- found a significant and positive correlation among these tor in its successful integration. These findings were corrob- factors. This finding was also validated by Nikou and orated by a recent study of 401 K-12 teachers by Yildiz Economides (2017), whose study revealed that effective atti- Durak (2021), on their TPACK level and technology integra- tudes, general usefulness, effort expectancy, and perceived tion, which suggested a significant correlation between playfulness are significant determinants of behavioral inten- teachers’ attitudes toward technology and its integration in tion to use technology. Teo (2008), reported a linkage their teaching-learning activities. The study further sug- between years of technology experience and a positive atti- gested establishing teachers’ positive views be the focal tude toward its acceptance and use while suggesting no vari- point of technology integration techniques in teachers’ edu- ation in terms of age or gender. More so, Hrtovnová et al. cation programs. However, establishing positive views of (2015), argue that age and gender do not impact the accep- teachers on technology requires an understanding of the fac- tance of e-learning. Li (2016), in a survey study of participat- tors that facilitates the development of such views. While ing teachers of a statewide professional development in prior studies emphasize influencing factors that are inherent China, acknowledged that the effectiveness of technology in the teachers themselves, which are related to personal integration can be influenced by the gender-based perspec- beliefs and views about technology integration, understand- tive before and after teachers’ involvement in professional ing other contextual factors such as social norms and cultural development. He argued that male teachers have shown more beliefs are equally important for establishing positive views enthusiasm and better attitudes regarding technology inte- and attitudes of teachers toward technology integration. gration in the classroom than their female counterparts, but Thus, the current study explored the implications of social less significant after professional development. However, influence on technology attitudes of engineering trainee- the same article reports more significant integration on teachers and its variability with respect to gender. behalf of the female teachers after the teachers participated Moreover, while some studies reported significant differ- in professional development activities geared toward tech- ences in these attitudes among teachers in terms of age, gen- nology integration. Abbasi et al. (2021), in their study using der, experience, anxiety, and subject domain, others reported surveys and interviews, found that undergraduate English non-significant differences in these variables. However, teachers had a favorable attitude toward ICT integration. none of these studies was conducted to investigate the trainee They also discovered no significant differences in the use of teachers’ attitudes in the engineering discipline. The present 4 SAGE Open study, therefore, aimed to investigate the trainee teachers’ users, rather “often said things like the internet is not for attitudes toward technology in the engineering discipline by someone like me” (p.11). Similarly, a global study on girls’ modifying the computer attitude scale (CAS) in relation to access and usage of mobile devices found that girls who measuring the social influence factor. It has been assumed undergo social restrictions on access to digital technology that the attitude of trainee-teachers of engineering education are susceptible to internalizing the idea that those devices are will provide valuable insights on the status of technology not safe and girls cannot be trusted with them (Girl Effect & integration in the engineering discipline so that necessary Vodafone Foundation, 2018). Thus, continuous reform of improvement can be provided. social norms related to technology use, especially in the rural areas is much needed. While the government established various programs, policies, and strategies to ensure digital Social Influence and Its Impact on Teachers’ inclusion in Bangladesh (Mou, 2016), there is a need to Technology Acceptance understand whether progress is being made with respect to Social influence is referred to “the degree to which an indi- the efforts that are put in place. vidual perceives that important others believe he or she At the moment, and to the best of our knowledge, no should use the new system” (Venkatesh et al., 2012, p. 159). empirical study has been conducted to investigate engineer- Important others mean people within the social circle of a ing education trainee teachers’ attitudes toward technology person that he or she considers important, such as family acceptance and use, in developing countries, or elsewhere. members, friends, and colleagues. It, therefore, implies the Technology, in this study, is considered as all sorts of infor- extent to which a person is influenced, encouraged, or moti- mation and communication technologies (ICT) that are used vated to use a particular system such as technology, by fam- in the teaching and learning contexts of engineering educa- ily members at home, friends at a social gathering, or tion. For example, computers (laptop and/or desktop), mobile colleagues at a workplace. While the use of digital devices phones, iPad, tablets, multi-media projectors, interactive within family and friends’ circles involves the social and per- smartboards, software, and other tools that are connected and sonal life of a person, an individual’s perception of the use of used for teaching and learning in engineering education. This such devices can easily intertwin with job-related use. In any is where the need of the present study becomes apparent, case, social norms prescribe what is considered acceptable which is necessary for effective engineering education behavior among various groups in the society, such as age, teacher preparation and training. The purpose of the study, gender, and ethnicity, and this obviously includes who has therefore, was to examine the trainee teachers’ attitudes access to digital technology and how it should be used toward technology use in tertiary engineering education in (Hernandez, 2019). This means positive or negative percep- Bangladesh, one of the developing countries in the world. tions of technology use due to societal norms can affect After carefully analyzing the need of conducting the present acceptance and use of such technologies at a professional study and to fill the current knowledge gap in the literature, level. For example, in Bangladesh where social norms led the following research questions were formulated: male family members to limit female members’ access to digital devices and/or monitor their usage to preserve family 1. What are the overall attitudes of engineering educa- reputation (Hernandez, 2019), female acceptance and use of tion trainee-teachers? digital technologies at the institutional level can hardly be 2. Is there any variation in the attitudes with respect to accomplished without facilitating a shift in cultural norms. age, gender, engineering specialization, perceived con- The social circle at the institutional level can help in reshap- fidence, and years of experience in technology use? ing the perceived cultural limitations (Huang et al., 2019), as highlighted by Kocaleva et al (2015), that teachers who Research Method already appreciate the relevance of technology at higher institutions can persuade and influence others to accept and In this study, a quantitative approach through a cross-sectional apply it to their pedagogical activities. More so, Durodolu survey research design was applied to investigate the attitude (2016), claimed that one of the important factors that encour- toward technology use of engineering trainee teachers in age teachers change their behavioral intention to use technol- Bangladesh. A cross-sectional survey design is commonly ogy is when they perceive the need from their fellow teachers. applied while collecting self-reported data such as opinions, However, those who self-internalized their limitations may attitudes, and values (Battaglia et al., 2008). Moreover, this find it difficult to break the barrier, even after realizing the survey design has been widely used in technology integra- potential benefits attributed to the use of digital technologies. tion studies in prior literature (Admiraal et al., 2017; M. A. For example, Croxson and Rowntree (2017), conducted a Hossain & Sormunen, 2019; Ifinedo et al., 2020; Reguera & study in Bangladesh on lower- and middle-class literate Lopez, 2021; Teo, 2008; Wei et al., 2016). A survey question- young adults aged 25 to 35, regarding mobile internet use. naire with a 5-point scale, measuring five constructs of the Though the participants ascribed positive attributes to inter- Extended CAS (Affective, Perceived Usefulness, Perceived net users, they do not however relate themselves as aspiring Ease of Use, Behavioral Intention, and Social Influence) was Salele and Khan 5 used to collect data from only those trainee teachers who Table 1. Demographics Data (N = 110). completed their first year of the training program. A detailed Male 66 explanation of the participants’ sample, data collection pro- Female 44 cedure, the instrument used, and data analysis technique is Age given in the following sub-sections. 15–24 62 25–34 41 Procedure 35 above 07 Domain The study was conducted during the 2017 to 2018 academic CEE 14 year. Initially, the instrument for data collection was devel- CSE 10 oped using four components of the computer attitude scale EEE 54 with the addition of the social influence component. After MCE 32 seeking permission to conduct the study from two selected universities in Bangladesh (Islamic University of Technology and Dhaka University), a cohort of 110 engineering trainee- Selwyn (1997), which has been reported by several research- teachers with prior experience in technology use were pur- ers to be a reliable instrument for measuring prospective posefully selected. The two universities were selected being teachers’ attitudes toward computer-related technologies. the only universities offering engineering teacher education For example, Sexton et al. (1999) used CAS in their study on programs as of the year 2017 when the research was con- prospective teachers and reported the CAS to have a high- ducted (see detailed discussion in section 3.2). At first, a pilot reliability coefficient (alpha = .90). More so, Teo (2008), study was conducted with 30 participants for an instrument claimed that CAS possesses a high-reliability coefficient reliability check. After that, data was collected from the (alpha = .86). However, other significant variables that influ- whole sample using a self-reported survey questionnaire (see ence computer attitudes such as subjective norms and facili- detailed discussion in section 3.3). At all times during the tating conditions are excluded in CAS, which may limit the data collection, one of the authors was present to respond to true interpretation of the participants’ attitudes (Teo et al., possible queries that may arise from the participants. It took 2008). In this study, the CAS was modified by adding one about 20 minutes on average for the participants to complete more variable from the subjective norms, to observe whether the survey questionnaire. Participants were also informed it has a significant influence on the attitudes toward com- that their participation is voluntary and they are free to with- puter-related technologies. The survey instrument used dur- draw their participation at any time. The responses were ing data collection comprised three sections. The first section tabulated in an MS Excel sheet and then transferred into IBM contained participants’ demographic data, such as age, gen- SPSS and AMOS for data screening and further analysis (see der, nationality, study program, and specialization. The sec- detailed discussion in section 3.4). Besides, information ond section contained participants’ years of technology related to participants was kept confidential and remained experience and perceived confidence. The third section col- anonymous without any direct link to the respondents. lected the participants’ responses to 24 items drawn from five constructs. The first four constructs were adopted from CAS namely: affect; perceived usefulness; perceived con- Sample trol; behavioral intention (Selwyn, 1997), while the fifth Participants were trainee teachers of two higher education construct Social Influence generated from subjective norms institutions in Bangladesh. These two universities were selected of TAM (Venkatesh & Davis, 2000) was included to modify because they are the only universities that provide teacher the CAS. Participants’ years of technology experience were training programs with engineering backgrounds in Bangladesh. obtained from the number of years the respondents claim to The participants were drawn from four areas of specializa- have been using technology. Perceived confidence was mea- tion. These include Computer Science and Engineering (CSE); sured with 5-point scale (very confident = 1; confident = 2; Civil and Environmental Engineering (CEE); Electrical and neutral = 3; timorous = 4; very timorous = 5). The degree to Electronic Engineering (EEE); Mechanical and Chemical which the participants agreed on the 24 items of the five con- Engineering (MCE). The number of questionnaires returned structs was also obtained using the same 5-point scale with no missing data is 110, with 66 male and 44 female par- (strongly agree = 1; agree = 2; neutral = 3; disagree = 4; ticipants. A total of 62 participants are between the age group strongly disagree = 5). (15–24), 41 are between the age group (25–34), while 7 partici- pants are between were age group (35 and above). Table 1 indi- cates the demographics of the participants. Data Analysis IBM SPSS and AMOS version 24 were employed during the Instrument data analysis. The scores from each item were aggregated to The instrument used in solving the research questions of this provide a corresponding score for each construct. In the case study was Computer Attitude Scale (CAS), adopted from of constructs with negative items, a reverse coding was 6 SAGE Open Table 2. Descriptive Statistics. N Minimum Maximum M S.D. Affective 110 1.50 5.00 3.90 0.802 Perceived use 110 3.00 5.00 4.40 0.559 Perceived control 110 1.50 5.00 3.60 0.784 Behavioral intention 110 1.00 5.00 3.66 1.13 Social influence 110 1.00 5.00 3.33 0.949 Overall technology attitude 110 2.50 5.00 3.95 0.611 performed so that meaningful analysis could be done. positive compared to their affective, intention to use, as well Exploratory Factor Analysis (EFA) was then conducted, to as their perception of usefulness and control of the technol- make sure further analysis with the data set is feasible. After ogy. In general, the participants’ overall attitude toward tech- that, Structural Equation Modeling (SEM) approach was nology was positive, with a mean score of 3.95. then employed to access the measurement model using max- imum likelihood estimation (MLE). Prior to the analysis, Attitudes Toward Technology With Respect to data screening was conducted and cases with missing data Age, Gender, Subject Specialization, and Years of values were removed to avoid complications, due to the sen- Technology sitivity of MLE to missing values. However, the final data set (N = 110) met the criteria for performing MLE (Ding et al., Age groups. The result of one-way MANOVA shows sig- 1995). To observe the variations of the respondents with nificant difference between age groups when considered on respect to their age, gender, subject specialization, and years the combined dependent variable Wilk’s lambda (Λ) = .741, of technology use, one-way MANOVA on the five constructs F(15, 281) = 2.161, p = .008, partial eta-squared (η ) = .095. To was performed for each independent variable (age, gender, determine which of the component(s) contributed to the sta- subject specialization, and years of technology use). Wilk’s tistically significant result, an ANOVA was performed against lambda (Λ) was reported in the analysis at a significant alpha each individual dependent variable at a significant alpha level level (.05). During MANOVA analysis, when an independent of .5. It was revealed from the result that, a significant variation variable shows a significant difference among the partici- exists on perceived usefulness: F(3, 106) = 4.03, p = .009, par- pants on the combined dependent variable, it means that tial eta-squared (η ) = .102 with age group 35 to 40 (M = 4.75) variation exists on one or more dependent variables among scoring highest, followed by age groups 25 to 35 (M = 4.59), the combined. Then, to discover which dependent variable(s) 15 to 25 (M = 4.27), and the lowest was scored by 40 to 45 contributed to the statistically significant result, ANOVA (M = 4.00); behavioral intention: F(3, 106) = 4.573, p = .005, analysis was further conducted for each individual depen- partial eta-squared (η ) = .115 with the corresponding scores dent variable. A bivariate correlation analysis was also per- of the participants of the age groups following the same fash- formed between years of technology use, confidence in using ion as that of perceived usefulness, such as age group 35 to technology, and overall attitudes toward technology, to 40 (M = 4.25) scored highest among all, followed by 25 to investigate whether a significant correlation exists between 35 (M = 4.05), and then 15 to 25 (M = 3.44) whereas the least them. was scored by 40 to 45 (M = 2.33). It was also evident from the result that, no significant variation exists on how much Results they liked technology (affective): F(3, 106) = 0.422, p = .738, partial eta-squared (η ) = .012, their perceived control, F(3, Overall Attitude Toward Technology 106) = 0.496, p = .686, partial eta-squared (η ) = .014, and The descriptive statistics of all the 24 items of the instrument how much they were influenced to use technology by the were presented based on five constructs in Table 2. Perceived society (social influence), F(3, 106) = 0.701, p = .554, partial usefulness has the highest mean score (4.40) followed by eta-squared (η ) = .019. The resulting effect of significant affective (3.90). The scores for behavioral intention and per- variations on perceived usefulness and behavioral intention ceived control are nearly the same (M = 3.66 and 3.60) while aggregated to a significant variation on the overall attitude on the social influence sub-scale has the lowest mean score technology by age. (M = 3.33). These scores show the participants’ perception of the usefulness of technology was higher than their percep- Gender. A MANOVA analysis for gender on the com- tion of control and behavioral intention. It was also revealed bined dependent variable shows that, a significant variation that the participants’ affective perception was less than that exists on at least one of the combined dependent variables: of their perception of usefulness. However, in the social Wilk’s lambda (Λ) = .879, F(5, 104) = 2.858, p = .018, par- influence sub-scale, the participants appear to be least tial eta-squared (η ) = .121. To further examine which of the Salele and Khan 7 combined dependent variables exhibit the significant varia- Correlation Analysis Between Years of Technology tion, and whether its magnitude influence the overall atti- Use, Perceived Confidence, and Attitude Toward tude, a separate ANOVA was performed. The result from Technology the ANOVA analysis shows that a significant variation exit on social influence only: F(1, 108) = 8.703, p = .004, partial To evaluate correlations between technology attitude, years eta-squared (η ) = .075, in which the female participants of technology experience and confidence, a bivariate corre- (M = 3.608) score higher than male participants (M = 3.121), lation was performed. The result revealed a significant cor- and that does not affect the overall attitude. No significant relation (r = .19, n = 110, p = .043) between technology variation exists on the affective: F(1, 108) = 1.421, p = .236, experience and confidence. More so, significant correlations partial eta squared (η ) = .013, perceived usefulness: F(1, were evident between technology attitude and experience 108) = 0.697, p = .406, partial eta squared (η ) = .006, (r = .240, n = 110, p = .011) and level of technology confi- perceived control: F(1, 108) = 0.156, p = .693, partial dence (r = .204, n = 110, p = .033). The mean years of tech- eta squared (η ) = .001, and behavioral intention: F(1, nology use was (M = 7.22, SD = 4.16), level of confidence 108) = 0.995, p = .321, partial eta squared (η ) = .009. In (M = 3.91, SD = .629), and the overall attitude (M = 3.96, general, the effect of significant variation realized between SD = 0.612). males and females on social influence does not result in a significant variation on the overall attitude on technology Exploratory Factor Analysis (EFA) with respect to gender. To evaluate the factor structure of the modified CAS, all Subject specialization. A one way between groups the 24 items of the scale were subjected to EFA. The MANOVA result shows a significant variation between sub- Kaiser-Meyer-Olkin measure confirmed that the sample was ject specialization on combined dependent variables: Wilk’s adequate, KMO = 0.730. Bartlett’s test of sphericity χ lambda (Λ) = .666, F(15, 282) = 2.980, p < .0001, partial (276) = 3,230.380, p < .001. This indicates a satisfactory cor- eta-squared (η ) = .127. An ANOVA was then performed relation structure for factor analysis. A five-factor solution against each dependent variable. A significant variation was was extracted using ML factor analysis, which accounts for observed among the four groups of specializations, on the 75.26 of the total variance (see Table 3). The threshold factor extent of their technology liking, F(3, 106) = 4.265, p = .007, loadings used was 0.4, and Kaiser eigenvalue >1, as recom- partial eta squared (η ) = .108, in which Civil Engineer- mended by Field (2009) and Johnson et al (2001). ing trainee-teachers scored highest (M = 4.57), Computer Remarkably, four of the five factors attained the same factor Science and Engineering trainees-teachers scored second structure as the previous study (Teo, 2008), except one item highest (M = 3.90), followed by Mechanical and Chemical of the affective factor (hesitation to use technology in front of engineering trainee-teachers (M = 3.83), and then the least other people) that loaded on behavioral intention. However, scored by Electrical and Electronic Engineering trainee- the item perfectly fits its new factor, since people’s percep- teachers (M = 3.76). Similarly, there was a significant dif- tion of the objective circumstances of a situation controls ference in how they perceived the usefulness of technology their psychological components in charge of their affect, (perceived usefulness): F(3, 106) = 4.719, p = .004, partial eta cognition, and behavior (Halevy et al., 2019). The resulting squared (η ) = .118, with Civil Engineering trainee-teachers factors from EFA are: (i) Perceived control with six items, having the highest score (M = 4.79), followed by Computer eigenvalue = 5.49, and percentage variance of 22.87%. (ii) Science and Engineering trainee-teachers (M = 4.70), then Affective component with five items, eigenvalue = 3.97, per- Electrical and Electronic Engineering trainee-teachers centage variance of 16.53%. (iii) Perceived usefulness with (M = 2.33) and the least scored by Mechanical and Chemi- five items, eigenvalue = 3.38 percentage variance of 14.11%. cal Engineering trainee-teachers (M = 4.25). Their perception (iv) Behavioral Intention with five items, eigenvalue = 12.25, also differ on perceived control, F(3, 106) = 5.722, p = .001, percentage variance = 12.25%. The new fifth factor intro- partial eta squared (η ) = .139. In the same way, Civil Engi- duced in this model; (v) Social influence, has three items neering scored highest (M = 4.29), Computer Science and with eigenvalue = 2.27, a percentage variance of 9.48%. Engineering (M = 3.90), Mechanical and Chemical Engi- These results suggest that the participants in Bangladesh per- neering trainee-teachers (M = 3.52), and lastly Electrical and ceived the same structure of the CAS found among the Electronic Engineering trainee-teachers (M = 3.43). There Singapore survey participants with the additional inclusion was no significant difference found between subject special- of the social influence factor (three items). izations on behavioral intention: F(3, 106) = 1.477, p = .225, partial eta squared (η ) = .040, and social influence: F(3, Normality Test 106) = 2.695, p = .050, partial eta squared (η ) = .071. The cumulative effects of these significant differences resulted To evaluate univariate normality, values of skewness and in a significant variation in the overall technology attitude in kurtosis for all the data set variables were examined. The terms of subject specialization. skewness values observed range from −0.695 to −0.141 8 SAGE Open Table 3. Exploratory Factor Analysis of 24 Items. Factor Item 1 2 3 4 5 Dimension PC2 0.954 −0.022 0.076 0.082 0.092 Perceived control PC6 0.929 −0.039 0.085 0.027 0.095 PC3 0.905 0.008 0.147 0.160 0.108 PC4 0.900 0.013 0.079 0.110 0.113 PC5 0.884 −0.027 0.114 0.160 0.033 PC1 0.602 −0.001 0.098 −0.181 −0.125 AF3 −0.113 0.911 −0.006 −0.026 −0.009 Affective component AF1 −0.131 0.903 −0.022 −0.021 0.014 AF4 0.076 0.894 0.101 0.045 −0.011 AF5 0.080 0.889 0.045 0.033 0.001 AF2 0.019 0.802 −0.016 0.038 0.058 PU1 0.147 0.050 0.956 −0.031 −0.008 Perceived usefulness PU4 0.115 0.028 0.949 −0.065 −0.032 PU2 −0.010 0.022 0.835 −0.056 −0.080 PU5 0.152 0.013 0.804 −0.051 −0.051 PU3 0.184 −0.017 0.594 0.284 0.209 BI2 0.032 0.051 −0.017 0.927 0.050 Behavioral intention BI4 0.084 0.021 −0.001 0.884 0.004 BI1 0.051 0.054 0.045 0.848 0.022 BI3 −0.028 0.064 −0.130 0.621 −0.363 BI5 0.076 −0.063 0.017 0.539 −0.012 SI2 0.033 0.063 −0.032 0.042 0.961 Social influence SI3 0.031 0.047 −0.006 0.033 0.936 SI1 0.136 −0.036 −0.023 −0.165 0.717 Note. Bold values are items loading for each factor extracted. Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in five iterations. while that of kurtosis ranges from −0.971 to 1.647 respec- from .84 to .95 for CR, .54 to .79 for AVE, and .840 to .935 tively. The results indicated that the sample achieved a nor- for α, demonstrating that convergent validity was achieved mal distribution based on the criteria ≤3 for skewness and (see Table 4). To access the discriminant validity, the square ≤8 for kurtosis (Kline, 2015). For assessing multivariate root of AVE for each factor was compared with its inter-fac- normality, Mardia’s coefficient, which was 249.794 in this tor correlation. A particular factor is assumed to have dis- study was compared with the computed value: 624 using the criminated from other factors when the value of the square formula p(p + 2), where p represents the number of items in root of its AVE is larger than all the values of its inter-factor the data set. Having Mardia’s coefficient lower than the com- correlations. (Fornell & Larker, 1981). It can be observed puted result from the formula indicates that multivariate nor- from Table 5 that, the value of the square root of AVE of mality was also achieved (Mardia, 1970; Raykov & each factor (highlighted) exceeds all its inter-factor correla- Marcoulides, 2008). tion values, suggesting that discriminant validity for all the factors was confirmed. The overall fit of the model was assessed by observing Confirmatory Factor Analysis (CFA) both absolute and incremental fit indices. These include: Test of the measurement model. The model was also asses- normed Chi square (χ /df); root mean square error of sed by employing CFA. In this analysis, the convergent and approximation (RMSEA); standardized root mean square discriminant validity, as well as the overall goodness of residual (SRMR); comparative fit index (CFI); and Tucker fit, were assessed. For the evaluation of convergent valid- Lewis Index (TLI). Corresponding values for theses indices ity, composite reliability (CR), average variance extracted in this model were (χ /df = 1.65; CFI = 0.96; TLI = 0.95; (AVE), and Cronbach’s alpha (α) were observed. The recom- RMSEA = 0.07; SRMR = 0.07), which indicates that, an mended adequate value for both CR and AVE is .5 or higher, acceptable model fit was achieved based on recommended and that of α should be greater than .70 (Fornell & Larker, threshold values by Hu et al. (2009): (χ /df < 3; CFI > 0.90; 1981; Hair, 2010). The observed values in this analysis range TLI > 0.90; RMSEA < 0.08; SRMR < 0.08). Salele and Khan 9 Table 4. Convergent Validity. Standardized factor Composite Average variance Cronbach’s alpha c a c b c c Construct Items loading (λ > .40) reliability (CR > .50) extracted (AVE > .50) (α > .80) Perceived control PC6 .938 .947 .755 .935 PC5 .868 PC4 .912 PC3 .940 PC2 .975 PC1 .481 Perceived usefulness PU5 .947 .950 .799 .891 PU4 .976 PU3 .517 PU2 .950 PU1 .989 Affective component AF5 .980 .897 .645 .928 AF4 .982 AF3 .687 AF2 .633 AF1 .655 Behavioral intention BI5 .401 .847 .548 .840 BI4 .927 BI3 .505 BI2 .956 BI1 .743 Social influence SI3 .962 .909 .774 .847 SI2 .987 SI1 .649 Σλ () Composite reliability = , (λ = standardized factor loading). ΣΣ λλ +− 1 () () b 2 Average variance extracted = Σ(λ) /n, (λ = standardized factor loading, n = number of items). Accepted threshold value. Discussion Table 5. Discriminant Validity. In general, the study revealed that the participants showed an PC PU AF BI SI overall positive attitude toward technology, being that each PC 0.87 subscale attained a mean score above the midpoint. The PU 0.22 0.89 attainment of a positive attitude toward technology by the AF 0.07 0.13 0.80 participants in this study could have been developed over BI 0.13 −0.04 0.08 0.74 time, due to government efforts to install technological cul- SI 0.15 −0.05 0.06 0.05 0.88 ture among the citizens, through various initiatives such as Note. Bold values are square roots of AVE from observed variables. “Access to Information (A21),” for over a decade, to enhance Non-bold values are correlations between constructs. PC = perceived technology use at various levels of education in Bangladesh control; PU = perceived usefulness; AF = affective; BI = behavioral intention; (Mou, 2016). There are also several projects and programs SI = social influence. that are geared toward training teachers and trainee-teachers on how to integrate technology into classroom instruction (Karim et al., 2017; Shohel & Kirkwood, 2012; Shohel & also revealed that, in technological abilities and perceived Power, 2010). ease of technology use, males rated themselves in different On the overall attitude toward technology, the current ways significantly, with males rated higher than females research does not reveal a significant variation between (Teo, 2014). A recent study on library and information sci- male and female participants. This contradicts earlier stud- ence students in Bangladesh also reported males with better ies that reported significant differences in attitudes toward technology skills than females (M. A. Hossain & Sormunen, technology between males and females (Padilla-Meléndez 2019). The absence of significant variation between males’ et al., 2013; Teo et al., 2015; Tezci, 2011). Other studies and females’ attitudes toward technology in this study is 10 SAGE Open consistent with studies that reported non-significant gender spurs a positive attitude toward technology. The higher the variation in attitudes toward technology (Bakr, 2011; Wong number of years of technology use, the higher the level of et al., 2012). For example, Bindu (2017) in a study on atti- confidence of the user. This signifies that technology use tude toward ICT among Indian teachers reported non- over time increases the level of confidence of the user, significant differences by gender. Hrtovnová et al. (2015), thereby resulting in a more positive attitude toward technol- also reported gender among several factors that had no sta- ogy. This finding corroborated the result of previous research tistically significant impact on technology acceptance by conducted by Wei et al (2016). More so, Teo (2008) claimed teachers. that one’s more frequent use of technology leads to one’s The positive attitude of females in this study could be attainment of varied technological skills, thereby promoting spurred by the social influence factor, being that the result in one’s overall knowledge of technology. This widens one’s this study shows a significant variation by gender in the way learning potential and prospects, which will consequently participants feel about societal encouragement on technol- promote a positive feeling toward technology. ogy use, with females being more influenced to use technol- Another major aim of the research was to investigate ogy by their social circle than males. This development whether social norms influence trainee-teachers attitudes according to Teo (2008), could be attributed to the changing toward technology, which was not included in previous socialization of females in today’s digital age, and the studies that employ CAS (Grover, 2016; Teo, 2008; Tezci, urgency to have a sense of belonging. This could eventually 2011). The findings of this research discovered that social lessen the barriers to technology acceptance among females. influence has contributed toward shaping the attitudes of Participants from different engineering specializations trainee-teachers toward technology use, especially among varied in their perception of affect, perceived usefulness, and the female participants. The result of the factor analysis also perceived control components. This variation could result indicates that social influence accounts for 9.48% of the from the fact that, in each specialization, there are exclusive variance, from the five factors extracted. It is therefore applications that may dictate how the participants feel about important to consider this additional factor when conducting the technology user interface, and thus result in varied per- studies with CAS. ceptions of the three components which are directly con- nected to the real use of technology. However, their Implications perception of behavioral intention and social influence was the same. This can be understood from the fact that behav- This study provides useful knowledge toward theory, prac- ioral intentional, as well as a social influence, are external to tice, and existing literature. More specifically, the study, the real use of the technology. Among all specializations, theoretically, contributed to research practice in technology participants from civil engineering and computer science and acceptance, by extending the computer attitude scale (CAS), engineering liked and perceived technology as useful for with social influence as an additional important factor to be their daily tasks more than participants from mechanical considered when conducting future research. Therefore, an engineering and electrical and electronic engineering. The extended computer attitude scale (CAS) is established for result also shows that participants from civil engineering and exploring newer research in this domain. In practice, the computer science engineering possessed the required skills findings of this study may serve as an insight into the prog- to use technology without the need for support, more than ress made so far, against the goals that are set to be achieved participants from mechanical engineering and electrical and in technology integration, especially in tertiary engineering electronic engineering. education, where technology impact both pedagogical prac- The differences found among students in different subject tice and professional practice of the learners as well. For specializations in this study are consistent with previous lit- example, the attainment of an overall positive attitude by erature. For example, Fakomogbon (2014) reported a signifi- both genders in the present study signified the positive cant difference in technology attitudes among secondary impact of government efforts to provide equal access to tech- school teachers with different subject domains. It could be nology for both genders (Z. Hossain et al., 2019). It has also possible that their perceptions were shaped by their job become apparent that societal norms should be one of the expectations (Teo, 2008). Trainee-teachers who expect to use important aspects to be handled for effective technology technology more frequently in their future carrier might have integration in Bangladesh and similar developing countries. perceived technology as more useful, and that they have The research also provides important information for more control over it, relative to those who expect less teacher trainers to consider for the course design of training encounter with technology in their teaching profession. In programs for pre-service teachers. In this way, trainee- general, participants from all subject specializations liked teacher can be best prepared on how to adopt technology in using technology and believed it has a positive impact on their teaching carrier. To existing literature, the study pro- their work. The findings of this research also indicated that vides an insight into a new segment of trainee teachers’ atti- frequent use of technology over time leads to the gradual tudes toward technology (Engineering Education) which has development of confidence by the user, and consequently not been reported in prior literature. Salele and Khan 11 usefulness and subsequently, stimulate their intention to use Limitations it. There was no gender variation in the overall positive atti- In general, there are three basic limitations to the current tude realized among the participants in this study. However, study. First, the collection of data was done through self- the findings from the analysis show that social influence is a reports from trainee-teachers, such that there is the potential vital construct, in addition to the four variables of the com- for self-response bias that may sway the true associations puter attitude scale (CAS), that influences the acceptance between variables, though this is common in all survey and intention to use technology by gender. Females’ attitudes research. To limit this potential bias, a combination of posi- in this regard were more likely swayed by their societal tive and negative items was used in the instrument to ensure norms than males. Further research may be conducted with a that true responses are received. The negative items were larger sample size. A Longitudinal design may also be then reverse coded after data collection to ensure meaningful employed to examine how the trainee teachers’ attitudes and analysis. Secondly, the participants in this study were engi- experiences change over time. While the additional variable neering education trainee-teachers and the sample size was (social influence) could be adopted for conducting other relatively small, and therefore, the generalization of the find- research with the computer attitude scale (CAS), other vari- ings is rather limited. However, the sample size had reached ables such as facilitating conditions and technological com- the minimum threshold recommended for the MLE analysis plexity may also be explored. technique employed in this study (Ding et al., 1995). Moreover, future studies may consider larger sample size. Acknowledgments Thirdly, the variables used in the instrument were basically The authors wish to thank Professor Timothy Teo, Murdoc determined by Computer Attitude Scale (CAS), though one University, Australia for sharing his validated tool. important variable (social norms) was included. However, other significant variables that may influence attitudes Declaration of Conflicting Interests toward technology were excluded and, may consequently The author(s) declared no potential conflicts of interest with respect lead to limited understanding of the trainee-teachers attitude to the research, authorship, and/or publication of this article. toward technology. Other limitations peculiar to the inclu- sion of the social influence component in the study include: Funding the number of male participants was higher than the number The author(s) received no financial support for the research, author- of female participants. This is a general variation in the engi- ship, and/or publication of this article. neering education of Bangladesh where male students enroll- ing in engineering programs always outnumber female ORCID iDs students (Jahan et al., 1998). Furthermore, the significance of social norms on technology acceptance and use obtained Nafiu Salele https://orcid.org/0000-0003-0756-3896 in this study is limited to trainee teachers, and may not be Md. Shahadat Hossain Khan https://orcid.org/0000-0003-0195 -1804 generalized to other groups such as in-service teachers and students, since their perception of the importance of their References social circle may differ from that of trainee teachers. Hence, more validation studies need to be conducted with samples Abbasi, W. T., Ibrahim, A. H., & Ali, F. B. (2021, 25–26 June). from segments other than trainee teachers. Finally, both uni- Perceptions about English as second language teachers’ versities where the study was conducted are located in the technology-based English language teaching in Pakistan: Attitudes, uses of technology and challenges [Conference ses- capital city, and social norms in the rural areas might influ- sion]. 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Engineering Trainee-Teachers’ Attitudes Toward Technology Use in Pedagogical Practices: Extending Computer Attitude Scale (CAS)

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© The Author(s) 2022
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2158-2440
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10.1177/21582440221102436
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Abstract

The current study examined the trainee teachers’ attitudes toward technology adoption and use in tertiary engineering education. The Computer Attitude Scale (CAS) was extended by including the social influence component, to examine whether social norms affect the acceptance of technology by teachers. Findings from 110 trainee-teachers revealed that their attitudes toward technology are positive. These attitudes constitute the way they like and intend to use technology, their perception of its usefulness in their daily tasks, and the control they perceived to have over technology while using it in engineering disciplines. The findings also confirm that social influence is an important predictor of trainee teachers’ attitudes toward using technology. Overall, the study provides a new influential factor (social) that could be merged with the other four major components (affect, perceived usefulness, perceived control, and behavioral intention) of CAS in conducting future research. The results of this study further provide useful knowledge that extends prior arguments concerning teachers’ attitudes toward using technology in teaching with respect to age, gender, and disciplines. More specifically, the study, theoretically, contributes to research practice in technology acceptance, by extending the computer attitude scale (CAS), with social influence as an additional important factor to be considered when conducting future research. Therefore, an extended CAS is established for exploring newer research in this domain. Policymakers and designers of teacher professional development will be informed of these findings that will accelerate initiatives of technology integration of engineering education in developing countries and other similar contexts. Keywords engineering education, technology use, teachers’, attitudes, trainee-teacher, Computer Attitude Scale and adopted (Almerich et al., 2016; Ifinedo et al., 2020). For Introduction example, Yarbro et al (2016) opined that as educators use The dynamics of today’s evolving technology are influenc- technology to improve students learning, the most important ing nearly every aspect of human life and are still changing role in technology integration is providing a learning envi- the way we do almost everything. The increasing involve- ronment that will support learners with active, hands-on, and ment of human-technology interaction in everyday tasks has authentic learning activities for offering enhanced myriad led to occupational and personal success (Bonina et al., 2021; learning experiences. Ultimately, teachers play a key role in Papanastasiou et al., 2019; Reis et al., 2018; Yildiz Durak, arranging technology-enhanced learning spaces (S. H. Khan, 2021). For students of today’s digital age to succeed in their 2015). Teachers, in a broader perspective, play a pivotal quest for knowledge and skills, there is a need for educa- role in realizing successful changes at all levels of education tional planners, policymakers, and practitioners to embrace (Van der Heijden et al., 2015). In relation to technology modern technology (Uerz et al., 2018). It is also imperative that classroom experiences be adequately equipped to pro- Islamic University of Technology, Gazipur, Dhaka, Bangladesh vide equitable and unbiased access to technological tools for Corresponding Author: students regardless of gender or ethnicity (Sahin et al., 2016; Md. Shahadat Hossain Khan, Department of Technical and Vocational Teo, 2008). Education, Islamic University of Technology, Room: 302, Academic In doing so, teachers are the motivational force through Building 1, Board Bazar, Gazipur, Dhaka 1704, Bangladesh. which technological tools can be introduced, implemented, Email: skha8285@iut-dhaka.edu Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open integration, a significant number of studies had explored the Related Literature positive impact of technology in the learning environment. Engineering Education and the Evolving Some of these include the research work of M. S. H. Khan et al., (2019) on the use of mobile devices in higher educa- Pedagogical Needs tion in Bangladesh, which yielded five ways of using mobile Engineering education consists of a synthesis of core math- devices in their learning that could have a positive impact on ematics and scientific principles, design concepts, and cut- student learning. Henderson et al (2017) conducted a study ting-edge technologies relevant to industry and research on how university students perceive the usefulness of digital jobs. It comprises the development of skills that will persist technologies in their learning. Their results revealed a wide for more than a few years after graduation and will serve as range of distinct digital benefits; such as flexibility of time, the basis for lifelong learning, enabling graduates to have a ability to communicate and collaborate at different places significant impact on society beyond graduation and for and locations, as well as retrieving, reviewing, and research- many years to come (Schor et al., 2021). It is also about mas- ing information. However, regardless of evidence that shows tering a variety of critical skills for managing cross-disci- the positive impacts of technology on educational practice, plinary projects. Engineering faculties all over the world are teachers’ resistance to its application at the instructional level frequently revising and modifying their curricula in response still exists in many cases (Seufert et al., 2021). to evolving trends, industry feedback, accreditation organi- Effective pedagogical use of technology among teachers zations, and a variety of other factors to ascertain that their remained negligible in Bangladesh where this research was graduates are equipped for an ever-changing world (Schor conducted (Fahadi & Khan, 2022). The government of et al., 2021). Bangladesh however, has shown its political will through Engineering education is crucial for humanitarian, social, various intervention programs aimed at promoting access to and economic development, and its graduates must be pre- technology, and its acceptance among teachers and students pared to address the everchanging sustainability concerns. (Obaydullah & Rahim, 2019). As part of the effort to The fourth industrial revolution, which is currently on the strengthen gender equity in digital literacy, the government political and industrial agenda, involving a widespread inte- made ICT a mandatory subject for all students from K6 gration of technologies such as automation, the Internet of through K12 and established a program that will ensure Things (IoT), artificial intelligence (AI), robotics, neuro- e-learning capacity among all female teachers by the year technologies, and virtual and augmented realities, is a more 2023 (Z. Hossain et al., 2019). However, having a technology- recent challenge (Hadgraft & Kolmos, 2020). A combination enabled learning environment, as well as training teachers, of these technologies intertwin in applications to transform does not guarantee effective pedagogical integration unless industrial processes into systems that are more connected, the teachers’ technological perceptions and attitudes are pos- reliable, predictable, and robust, with a high degree of cer- itive (Farjon et al., 2019; Khan & Hasan, 2013). Even though tainty (Gupta, 2020). technology integration is guided by government policies, As a result, “engineering education is experiencing a teachers still hold the autonomy to decide when and how to paradigm shift from teacher-centric to the student-centric use it (Teo, 2011). It, therefore, becomes imperative to have teaching-learning process, content-based education to out- an insight into the teachers’ attitudes toward technology, so come-based education, knowledge-seeking to knowledge that progress related to possible successful technology inte- sharing classrooms, teachers to facilitators, traditional engi- gration can be obtained. Moreover, true insight can best be neering disciplines to interdisciplinary courses, chalk and sought from trainee-teachers, who are in the making, and board (lecture-based) learning to technology-driven learn- whose attitudes are less likely to be swayed by the old peda- ing” (Pavai Madheswari & Uma Mageswari, 2020, p. 01). gogical trends. This paradigm shift results in a new era of engineering edu- There is a global outcry for the need to support skill- cation known as “Engineering Education 4.0” (Frerich et al., building for students in science technology engineering 2016). Engineering Education 4.0 involves the implementa- and mathematics (STEM) to enable them to thrive in tion of current and emerging technologies combined with today’s technology-driven era (Rifandi et al., 2019). This innovative pedagogical approaches inspired by the fourth has given birth to the urgent need to investigate the techno- industrial revolution (IR4.0) for flawlessness, satisfaction, logical attitudes of trainee-teachers in this segment of edu- time-saving, skill development, and efficiency enhancement cation, particularly engineering education trainee-teachers. in engineering education (Gupta, 2020). While acknowledg- This study focuses on the attitudes toward the technology ing the fact that the students of today’s engineering education of engineering trainee-teachers in a developing country. are born in the digital age, and are normally referred to as This research may be crucial for further improvements in generation Z (Opri & Ionescu, 2020), which makes them engineering education teacher professional development capable of withstanding technological challenges posed by programs (ETPD), in Bangladesh and other countries with the evolving fourth industrial revolution (IR 4.0), there are similar contexts. various factors outside the control of the students, such as Salele and Khan 3 digital divide issues related to access to technology and more ICT tools by teachers based on their gender or educational importantly conservative pedagogical approaches adopted qualifications. The only variation identified between lectur- by instructors (Gonçalves & Capucha, 2020; Salem & ers was attributed to their competence, which is normally Mohammadzadeh, 2018), the latter of which has become a accumulated over time, thus implying the impact of years of major concern in technology integration studies (Arkorful technology use on its efficient use. Recently, Shodipe and et al., 2021; Pamuk, 2022; Papadakis, 2018; Raman et al., Ohanu (2021), surveyed 418 electrical and electronic tech- 2015; Sánchez-Prieto et al., 2019; Shah et al., 2020; Shodipe nology teachers in higher education about their attitudes, & Ohanu, 2021; Wijnen et al., 2021; M. Xu et al., 2021; S. engagement, and disposition toward mobile learning. They Xu & Zhu, 2020; Yildiz Durak, 2021). It is therefore impor- discovered a positive correlation between teachers’ per- tant than ever, for engineering education teachers and instruc- ceived ease of use and actual use of mobile learning, as well tors to embrace and use the evolving technology-driven as a positive correlation between teachers’ disposition and pedagogical approaches, with the goal of improving stu- perceived ease of use, which forms the foundation of human dents’ engagement and content delivery, so that students can attitude and behavior (Shodipe & Ohanu, 2021). In addition, achieve the feat essential for evolving engineering practice. Saefuddin et al. (2019), reported an overall positive attitude toward technology by science teachers surveyed in the south- east province of Indonesia. However, the study identified a Teachers’ Attitudes Toward Technology Use small percentage of the teachers who do not agree that tech- The attitudes and beliefs of teachers toward technology are nology is an efficient communication and presentation tool crucial for school leaders to understand as schools shift for effective teaching and learning activities. S. Xu and Zhu toward modern digital pedagogy (Mou, 2016; Shah et al., (2020), identified key factors that affect teachers’ acceptance 2020). Subdomains of attitudes toward technology include and integration of technology as attitude and technology perceived usefulness, control, liking behavioral intention, beliefs, and self-efficacy with respect to technology use. and confidence (Mahajan, 2016). Other factors include age, They also identified a positive correlation between attitude gender (Hrtovnová et al., 2015; Teo, 2014), technology con- and technology belief and intention to integrate technology fidence (Miller et al., 2017), anxiety (Chiu & Churchill, in teaching. In research conducted by Farjon et al. (2019), on 2016), and self-efficacy (Brantley, 2018). Baturay et al. the technology integration of pre-service teachers, it was (2017), in a study on “the relationship among pre-service revealed that although access to technology has an impact on teachers’ computer competence, attitude toward computer- pre-service teachers’ technology integration, their attitudes assisted education, and intention of technology acceptance,” and views about technology integration remain a major fac- found a significant and positive correlation among these tor in its successful integration. These findings were corrob- factors. This finding was also validated by Nikou and orated by a recent study of 401 K-12 teachers by Yildiz Economides (2017), whose study revealed that effective atti- Durak (2021), on their TPACK level and technology integra- tudes, general usefulness, effort expectancy, and perceived tion, which suggested a significant correlation between playfulness are significant determinants of behavioral inten- teachers’ attitudes toward technology and its integration in tion to use technology. Teo (2008), reported a linkage their teaching-learning activities. The study further sug- between years of technology experience and a positive atti- gested establishing teachers’ positive views be the focal tude toward its acceptance and use while suggesting no vari- point of technology integration techniques in teachers’ edu- ation in terms of age or gender. More so, Hrtovnová et al. cation programs. However, establishing positive views of (2015), argue that age and gender do not impact the accep- teachers on technology requires an understanding of the fac- tance of e-learning. Li (2016), in a survey study of participat- tors that facilitates the development of such views. While ing teachers of a statewide professional development in prior studies emphasize influencing factors that are inherent China, acknowledged that the effectiveness of technology in the teachers themselves, which are related to personal integration can be influenced by the gender-based perspec- beliefs and views about technology integration, understand- tive before and after teachers’ involvement in professional ing other contextual factors such as social norms and cultural development. He argued that male teachers have shown more beliefs are equally important for establishing positive views enthusiasm and better attitudes regarding technology inte- and attitudes of teachers toward technology integration. gration in the classroom than their female counterparts, but Thus, the current study explored the implications of social less significant after professional development. However, influence on technology attitudes of engineering trainee- the same article reports more significant integration on teachers and its variability with respect to gender. behalf of the female teachers after the teachers participated Moreover, while some studies reported significant differ- in professional development activities geared toward tech- ences in these attitudes among teachers in terms of age, gen- nology integration. Abbasi et al. (2021), in their study using der, experience, anxiety, and subject domain, others reported surveys and interviews, found that undergraduate English non-significant differences in these variables. However, teachers had a favorable attitude toward ICT integration. none of these studies was conducted to investigate the trainee They also discovered no significant differences in the use of teachers’ attitudes in the engineering discipline. The present 4 SAGE Open study, therefore, aimed to investigate the trainee teachers’ users, rather “often said things like the internet is not for attitudes toward technology in the engineering discipline by someone like me” (p.11). Similarly, a global study on girls’ modifying the computer attitude scale (CAS) in relation to access and usage of mobile devices found that girls who measuring the social influence factor. It has been assumed undergo social restrictions on access to digital technology that the attitude of trainee-teachers of engineering education are susceptible to internalizing the idea that those devices are will provide valuable insights on the status of technology not safe and girls cannot be trusted with them (Girl Effect & integration in the engineering discipline so that necessary Vodafone Foundation, 2018). Thus, continuous reform of improvement can be provided. social norms related to technology use, especially in the rural areas is much needed. While the government established various programs, policies, and strategies to ensure digital Social Influence and Its Impact on Teachers’ inclusion in Bangladesh (Mou, 2016), there is a need to Technology Acceptance understand whether progress is being made with respect to Social influence is referred to “the degree to which an indi- the efforts that are put in place. vidual perceives that important others believe he or she At the moment, and to the best of our knowledge, no should use the new system” (Venkatesh et al., 2012, p. 159). empirical study has been conducted to investigate engineer- Important others mean people within the social circle of a ing education trainee teachers’ attitudes toward technology person that he or she considers important, such as family acceptance and use, in developing countries, or elsewhere. members, friends, and colleagues. It, therefore, implies the Technology, in this study, is considered as all sorts of infor- extent to which a person is influenced, encouraged, or moti- mation and communication technologies (ICT) that are used vated to use a particular system such as technology, by fam- in the teaching and learning contexts of engineering educa- ily members at home, friends at a social gathering, or tion. For example, computers (laptop and/or desktop), mobile colleagues at a workplace. While the use of digital devices phones, iPad, tablets, multi-media projectors, interactive within family and friends’ circles involves the social and per- smartboards, software, and other tools that are connected and sonal life of a person, an individual’s perception of the use of used for teaching and learning in engineering education. This such devices can easily intertwin with job-related use. In any is where the need of the present study becomes apparent, case, social norms prescribe what is considered acceptable which is necessary for effective engineering education behavior among various groups in the society, such as age, teacher preparation and training. The purpose of the study, gender, and ethnicity, and this obviously includes who has therefore, was to examine the trainee teachers’ attitudes access to digital technology and how it should be used toward technology use in tertiary engineering education in (Hernandez, 2019). This means positive or negative percep- Bangladesh, one of the developing countries in the world. tions of technology use due to societal norms can affect After carefully analyzing the need of conducting the present acceptance and use of such technologies at a professional study and to fill the current knowledge gap in the literature, level. For example, in Bangladesh where social norms led the following research questions were formulated: male family members to limit female members’ access to digital devices and/or monitor their usage to preserve family 1. What are the overall attitudes of engineering educa- reputation (Hernandez, 2019), female acceptance and use of tion trainee-teachers? digital technologies at the institutional level can hardly be 2. Is there any variation in the attitudes with respect to accomplished without facilitating a shift in cultural norms. age, gender, engineering specialization, perceived con- The social circle at the institutional level can help in reshap- fidence, and years of experience in technology use? ing the perceived cultural limitations (Huang et al., 2019), as highlighted by Kocaleva et al (2015), that teachers who Research Method already appreciate the relevance of technology at higher institutions can persuade and influence others to accept and In this study, a quantitative approach through a cross-sectional apply it to their pedagogical activities. More so, Durodolu survey research design was applied to investigate the attitude (2016), claimed that one of the important factors that encour- toward technology use of engineering trainee teachers in age teachers change their behavioral intention to use technol- Bangladesh. A cross-sectional survey design is commonly ogy is when they perceive the need from their fellow teachers. applied while collecting self-reported data such as opinions, However, those who self-internalized their limitations may attitudes, and values (Battaglia et al., 2008). Moreover, this find it difficult to break the barrier, even after realizing the survey design has been widely used in technology integra- potential benefits attributed to the use of digital technologies. tion studies in prior literature (Admiraal et al., 2017; M. A. For example, Croxson and Rowntree (2017), conducted a Hossain & Sormunen, 2019; Ifinedo et al., 2020; Reguera & study in Bangladesh on lower- and middle-class literate Lopez, 2021; Teo, 2008; Wei et al., 2016). A survey question- young adults aged 25 to 35, regarding mobile internet use. naire with a 5-point scale, measuring five constructs of the Though the participants ascribed positive attributes to inter- Extended CAS (Affective, Perceived Usefulness, Perceived net users, they do not however relate themselves as aspiring Ease of Use, Behavioral Intention, and Social Influence) was Salele and Khan 5 used to collect data from only those trainee teachers who Table 1. Demographics Data (N = 110). completed their first year of the training program. A detailed Male 66 explanation of the participants’ sample, data collection pro- Female 44 cedure, the instrument used, and data analysis technique is Age given in the following sub-sections. 15–24 62 25–34 41 Procedure 35 above 07 Domain The study was conducted during the 2017 to 2018 academic CEE 14 year. Initially, the instrument for data collection was devel- CSE 10 oped using four components of the computer attitude scale EEE 54 with the addition of the social influence component. After MCE 32 seeking permission to conduct the study from two selected universities in Bangladesh (Islamic University of Technology and Dhaka University), a cohort of 110 engineering trainee- Selwyn (1997), which has been reported by several research- teachers with prior experience in technology use were pur- ers to be a reliable instrument for measuring prospective posefully selected. The two universities were selected being teachers’ attitudes toward computer-related technologies. the only universities offering engineering teacher education For example, Sexton et al. (1999) used CAS in their study on programs as of the year 2017 when the research was con- prospective teachers and reported the CAS to have a high- ducted (see detailed discussion in section 3.2). At first, a pilot reliability coefficient (alpha = .90). More so, Teo (2008), study was conducted with 30 participants for an instrument claimed that CAS possesses a high-reliability coefficient reliability check. After that, data was collected from the (alpha = .86). However, other significant variables that influ- whole sample using a self-reported survey questionnaire (see ence computer attitudes such as subjective norms and facili- detailed discussion in section 3.3). At all times during the tating conditions are excluded in CAS, which may limit the data collection, one of the authors was present to respond to true interpretation of the participants’ attitudes (Teo et al., possible queries that may arise from the participants. It took 2008). In this study, the CAS was modified by adding one about 20 minutes on average for the participants to complete more variable from the subjective norms, to observe whether the survey questionnaire. Participants were also informed it has a significant influence on the attitudes toward com- that their participation is voluntary and they are free to with- puter-related technologies. The survey instrument used dur- draw their participation at any time. The responses were ing data collection comprised three sections. The first section tabulated in an MS Excel sheet and then transferred into IBM contained participants’ demographic data, such as age, gen- SPSS and AMOS for data screening and further analysis (see der, nationality, study program, and specialization. The sec- detailed discussion in section 3.4). Besides, information ond section contained participants’ years of technology related to participants was kept confidential and remained experience and perceived confidence. The third section col- anonymous without any direct link to the respondents. lected the participants’ responses to 24 items drawn from five constructs. The first four constructs were adopted from CAS namely: affect; perceived usefulness; perceived con- Sample trol; behavioral intention (Selwyn, 1997), while the fifth Participants were trainee teachers of two higher education construct Social Influence generated from subjective norms institutions in Bangladesh. These two universities were selected of TAM (Venkatesh & Davis, 2000) was included to modify because they are the only universities that provide teacher the CAS. Participants’ years of technology experience were training programs with engineering backgrounds in Bangladesh. obtained from the number of years the respondents claim to The participants were drawn from four areas of specializa- have been using technology. Perceived confidence was mea- tion. These include Computer Science and Engineering (CSE); sured with 5-point scale (very confident = 1; confident = 2; Civil and Environmental Engineering (CEE); Electrical and neutral = 3; timorous = 4; very timorous = 5). The degree to Electronic Engineering (EEE); Mechanical and Chemical which the participants agreed on the 24 items of the five con- Engineering (MCE). The number of questionnaires returned structs was also obtained using the same 5-point scale with no missing data is 110, with 66 male and 44 female par- (strongly agree = 1; agree = 2; neutral = 3; disagree = 4; ticipants. A total of 62 participants are between the age group strongly disagree = 5). (15–24), 41 are between the age group (25–34), while 7 partici- pants are between were age group (35 and above). Table 1 indi- cates the demographics of the participants. Data Analysis IBM SPSS and AMOS version 24 were employed during the Instrument data analysis. The scores from each item were aggregated to The instrument used in solving the research questions of this provide a corresponding score for each construct. In the case study was Computer Attitude Scale (CAS), adopted from of constructs with negative items, a reverse coding was 6 SAGE Open Table 2. Descriptive Statistics. N Minimum Maximum M S.D. Affective 110 1.50 5.00 3.90 0.802 Perceived use 110 3.00 5.00 4.40 0.559 Perceived control 110 1.50 5.00 3.60 0.784 Behavioral intention 110 1.00 5.00 3.66 1.13 Social influence 110 1.00 5.00 3.33 0.949 Overall technology attitude 110 2.50 5.00 3.95 0.611 performed so that meaningful analysis could be done. positive compared to their affective, intention to use, as well Exploratory Factor Analysis (EFA) was then conducted, to as their perception of usefulness and control of the technol- make sure further analysis with the data set is feasible. After ogy. In general, the participants’ overall attitude toward tech- that, Structural Equation Modeling (SEM) approach was nology was positive, with a mean score of 3.95. then employed to access the measurement model using max- imum likelihood estimation (MLE). Prior to the analysis, Attitudes Toward Technology With Respect to data screening was conducted and cases with missing data Age, Gender, Subject Specialization, and Years of values were removed to avoid complications, due to the sen- Technology sitivity of MLE to missing values. However, the final data set (N = 110) met the criteria for performing MLE (Ding et al., Age groups. The result of one-way MANOVA shows sig- 1995). To observe the variations of the respondents with nificant difference between age groups when considered on respect to their age, gender, subject specialization, and years the combined dependent variable Wilk’s lambda (Λ) = .741, of technology use, one-way MANOVA on the five constructs F(15, 281) = 2.161, p = .008, partial eta-squared (η ) = .095. To was performed for each independent variable (age, gender, determine which of the component(s) contributed to the sta- subject specialization, and years of technology use). Wilk’s tistically significant result, an ANOVA was performed against lambda (Λ) was reported in the analysis at a significant alpha each individual dependent variable at a significant alpha level level (.05). During MANOVA analysis, when an independent of .5. It was revealed from the result that, a significant variation variable shows a significant difference among the partici- exists on perceived usefulness: F(3, 106) = 4.03, p = .009, par- pants on the combined dependent variable, it means that tial eta-squared (η ) = .102 with age group 35 to 40 (M = 4.75) variation exists on one or more dependent variables among scoring highest, followed by age groups 25 to 35 (M = 4.59), the combined. Then, to discover which dependent variable(s) 15 to 25 (M = 4.27), and the lowest was scored by 40 to 45 contributed to the statistically significant result, ANOVA (M = 4.00); behavioral intention: F(3, 106) = 4.573, p = .005, analysis was further conducted for each individual depen- partial eta-squared (η ) = .115 with the corresponding scores dent variable. A bivariate correlation analysis was also per- of the participants of the age groups following the same fash- formed between years of technology use, confidence in using ion as that of perceived usefulness, such as age group 35 to technology, and overall attitudes toward technology, to 40 (M = 4.25) scored highest among all, followed by 25 to investigate whether a significant correlation exists between 35 (M = 4.05), and then 15 to 25 (M = 3.44) whereas the least them. was scored by 40 to 45 (M = 2.33). It was also evident from the result that, no significant variation exists on how much Results they liked technology (affective): F(3, 106) = 0.422, p = .738, partial eta-squared (η ) = .012, their perceived control, F(3, Overall Attitude Toward Technology 106) = 0.496, p = .686, partial eta-squared (η ) = .014, and The descriptive statistics of all the 24 items of the instrument how much they were influenced to use technology by the were presented based on five constructs in Table 2. Perceived society (social influence), F(3, 106) = 0.701, p = .554, partial usefulness has the highest mean score (4.40) followed by eta-squared (η ) = .019. The resulting effect of significant affective (3.90). The scores for behavioral intention and per- variations on perceived usefulness and behavioral intention ceived control are nearly the same (M = 3.66 and 3.60) while aggregated to a significant variation on the overall attitude on the social influence sub-scale has the lowest mean score technology by age. (M = 3.33). These scores show the participants’ perception of the usefulness of technology was higher than their percep- Gender. A MANOVA analysis for gender on the com- tion of control and behavioral intention. It was also revealed bined dependent variable shows that, a significant variation that the participants’ affective perception was less than that exists on at least one of the combined dependent variables: of their perception of usefulness. However, in the social Wilk’s lambda (Λ) = .879, F(5, 104) = 2.858, p = .018, par- influence sub-scale, the participants appear to be least tial eta-squared (η ) = .121. To further examine which of the Salele and Khan 7 combined dependent variables exhibit the significant varia- Correlation Analysis Between Years of Technology tion, and whether its magnitude influence the overall atti- Use, Perceived Confidence, and Attitude Toward tude, a separate ANOVA was performed. The result from Technology the ANOVA analysis shows that a significant variation exit on social influence only: F(1, 108) = 8.703, p = .004, partial To evaluate correlations between technology attitude, years eta-squared (η ) = .075, in which the female participants of technology experience and confidence, a bivariate corre- (M = 3.608) score higher than male participants (M = 3.121), lation was performed. The result revealed a significant cor- and that does not affect the overall attitude. No significant relation (r = .19, n = 110, p = .043) between technology variation exists on the affective: F(1, 108) = 1.421, p = .236, experience and confidence. More so, significant correlations partial eta squared (η ) = .013, perceived usefulness: F(1, were evident between technology attitude and experience 108) = 0.697, p = .406, partial eta squared (η ) = .006, (r = .240, n = 110, p = .011) and level of technology confi- perceived control: F(1, 108) = 0.156, p = .693, partial dence (r = .204, n = 110, p = .033). The mean years of tech- eta squared (η ) = .001, and behavioral intention: F(1, nology use was (M = 7.22, SD = 4.16), level of confidence 108) = 0.995, p = .321, partial eta squared (η ) = .009. In (M = 3.91, SD = .629), and the overall attitude (M = 3.96, general, the effect of significant variation realized between SD = 0.612). males and females on social influence does not result in a significant variation on the overall attitude on technology Exploratory Factor Analysis (EFA) with respect to gender. To evaluate the factor structure of the modified CAS, all Subject specialization. A one way between groups the 24 items of the scale were subjected to EFA. The MANOVA result shows a significant variation between sub- Kaiser-Meyer-Olkin measure confirmed that the sample was ject specialization on combined dependent variables: Wilk’s adequate, KMO = 0.730. Bartlett’s test of sphericity χ lambda (Λ) = .666, F(15, 282) = 2.980, p < .0001, partial (276) = 3,230.380, p < .001. This indicates a satisfactory cor- eta-squared (η ) = .127. An ANOVA was then performed relation structure for factor analysis. A five-factor solution against each dependent variable. A significant variation was was extracted using ML factor analysis, which accounts for observed among the four groups of specializations, on the 75.26 of the total variance (see Table 3). The threshold factor extent of their technology liking, F(3, 106) = 4.265, p = .007, loadings used was 0.4, and Kaiser eigenvalue >1, as recom- partial eta squared (η ) = .108, in which Civil Engineer- mended by Field (2009) and Johnson et al (2001). ing trainee-teachers scored highest (M = 4.57), Computer Remarkably, four of the five factors attained the same factor Science and Engineering trainees-teachers scored second structure as the previous study (Teo, 2008), except one item highest (M = 3.90), followed by Mechanical and Chemical of the affective factor (hesitation to use technology in front of engineering trainee-teachers (M = 3.83), and then the least other people) that loaded on behavioral intention. However, scored by Electrical and Electronic Engineering trainee- the item perfectly fits its new factor, since people’s percep- teachers (M = 3.76). Similarly, there was a significant dif- tion of the objective circumstances of a situation controls ference in how they perceived the usefulness of technology their psychological components in charge of their affect, (perceived usefulness): F(3, 106) = 4.719, p = .004, partial eta cognition, and behavior (Halevy et al., 2019). The resulting squared (η ) = .118, with Civil Engineering trainee-teachers factors from EFA are: (i) Perceived control with six items, having the highest score (M = 4.79), followed by Computer eigenvalue = 5.49, and percentage variance of 22.87%. (ii) Science and Engineering trainee-teachers (M = 4.70), then Affective component with five items, eigenvalue = 3.97, per- Electrical and Electronic Engineering trainee-teachers centage variance of 16.53%. (iii) Perceived usefulness with (M = 2.33) and the least scored by Mechanical and Chemi- five items, eigenvalue = 3.38 percentage variance of 14.11%. cal Engineering trainee-teachers (M = 4.25). Their perception (iv) Behavioral Intention with five items, eigenvalue = 12.25, also differ on perceived control, F(3, 106) = 5.722, p = .001, percentage variance = 12.25%. The new fifth factor intro- partial eta squared (η ) = .139. In the same way, Civil Engi- duced in this model; (v) Social influence, has three items neering scored highest (M = 4.29), Computer Science and with eigenvalue = 2.27, a percentage variance of 9.48%. Engineering (M = 3.90), Mechanical and Chemical Engi- These results suggest that the participants in Bangladesh per- neering trainee-teachers (M = 3.52), and lastly Electrical and ceived the same structure of the CAS found among the Electronic Engineering trainee-teachers (M = 3.43). There Singapore survey participants with the additional inclusion was no significant difference found between subject special- of the social influence factor (three items). izations on behavioral intention: F(3, 106) = 1.477, p = .225, partial eta squared (η ) = .040, and social influence: F(3, Normality Test 106) = 2.695, p = .050, partial eta squared (η ) = .071. The cumulative effects of these significant differences resulted To evaluate univariate normality, values of skewness and in a significant variation in the overall technology attitude in kurtosis for all the data set variables were examined. The terms of subject specialization. skewness values observed range from −0.695 to −0.141 8 SAGE Open Table 3. Exploratory Factor Analysis of 24 Items. Factor Item 1 2 3 4 5 Dimension PC2 0.954 −0.022 0.076 0.082 0.092 Perceived control PC6 0.929 −0.039 0.085 0.027 0.095 PC3 0.905 0.008 0.147 0.160 0.108 PC4 0.900 0.013 0.079 0.110 0.113 PC5 0.884 −0.027 0.114 0.160 0.033 PC1 0.602 −0.001 0.098 −0.181 −0.125 AF3 −0.113 0.911 −0.006 −0.026 −0.009 Affective component AF1 −0.131 0.903 −0.022 −0.021 0.014 AF4 0.076 0.894 0.101 0.045 −0.011 AF5 0.080 0.889 0.045 0.033 0.001 AF2 0.019 0.802 −0.016 0.038 0.058 PU1 0.147 0.050 0.956 −0.031 −0.008 Perceived usefulness PU4 0.115 0.028 0.949 −0.065 −0.032 PU2 −0.010 0.022 0.835 −0.056 −0.080 PU5 0.152 0.013 0.804 −0.051 −0.051 PU3 0.184 −0.017 0.594 0.284 0.209 BI2 0.032 0.051 −0.017 0.927 0.050 Behavioral intention BI4 0.084 0.021 −0.001 0.884 0.004 BI1 0.051 0.054 0.045 0.848 0.022 BI3 −0.028 0.064 −0.130 0.621 −0.363 BI5 0.076 −0.063 0.017 0.539 −0.012 SI2 0.033 0.063 −0.032 0.042 0.961 Social influence SI3 0.031 0.047 −0.006 0.033 0.936 SI1 0.136 −0.036 −0.023 −0.165 0.717 Note. Bold values are items loading for each factor extracted. Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in five iterations. while that of kurtosis ranges from −0.971 to 1.647 respec- from .84 to .95 for CR, .54 to .79 for AVE, and .840 to .935 tively. The results indicated that the sample achieved a nor- for α, demonstrating that convergent validity was achieved mal distribution based on the criteria ≤3 for skewness and (see Table 4). To access the discriminant validity, the square ≤8 for kurtosis (Kline, 2015). For assessing multivariate root of AVE for each factor was compared with its inter-fac- normality, Mardia’s coefficient, which was 249.794 in this tor correlation. A particular factor is assumed to have dis- study was compared with the computed value: 624 using the criminated from other factors when the value of the square formula p(p + 2), where p represents the number of items in root of its AVE is larger than all the values of its inter-factor the data set. Having Mardia’s coefficient lower than the com- correlations. (Fornell & Larker, 1981). It can be observed puted result from the formula indicates that multivariate nor- from Table 5 that, the value of the square root of AVE of mality was also achieved (Mardia, 1970; Raykov & each factor (highlighted) exceeds all its inter-factor correla- Marcoulides, 2008). tion values, suggesting that discriminant validity for all the factors was confirmed. The overall fit of the model was assessed by observing Confirmatory Factor Analysis (CFA) both absolute and incremental fit indices. These include: Test of the measurement model. The model was also asses- normed Chi square (χ /df); root mean square error of sed by employing CFA. In this analysis, the convergent and approximation (RMSEA); standardized root mean square discriminant validity, as well as the overall goodness of residual (SRMR); comparative fit index (CFI); and Tucker fit, were assessed. For the evaluation of convergent valid- Lewis Index (TLI). Corresponding values for theses indices ity, composite reliability (CR), average variance extracted in this model were (χ /df = 1.65; CFI = 0.96; TLI = 0.95; (AVE), and Cronbach’s alpha (α) were observed. The recom- RMSEA = 0.07; SRMR = 0.07), which indicates that, an mended adequate value for both CR and AVE is .5 or higher, acceptable model fit was achieved based on recommended and that of α should be greater than .70 (Fornell & Larker, threshold values by Hu et al. (2009): (χ /df < 3; CFI > 0.90; 1981; Hair, 2010). The observed values in this analysis range TLI > 0.90; RMSEA < 0.08; SRMR < 0.08). Salele and Khan 9 Table 4. Convergent Validity. Standardized factor Composite Average variance Cronbach’s alpha c a c b c c Construct Items loading (λ > .40) reliability (CR > .50) extracted (AVE > .50) (α > .80) Perceived control PC6 .938 .947 .755 .935 PC5 .868 PC4 .912 PC3 .940 PC2 .975 PC1 .481 Perceived usefulness PU5 .947 .950 .799 .891 PU4 .976 PU3 .517 PU2 .950 PU1 .989 Affective component AF5 .980 .897 .645 .928 AF4 .982 AF3 .687 AF2 .633 AF1 .655 Behavioral intention BI5 .401 .847 .548 .840 BI4 .927 BI3 .505 BI2 .956 BI1 .743 Social influence SI3 .962 .909 .774 .847 SI2 .987 SI1 .649 Σλ () Composite reliability = , (λ = standardized factor loading). ΣΣ λλ +− 1 () () b 2 Average variance extracted = Σ(λ) /n, (λ = standardized factor loading, n = number of items). Accepted threshold value. Discussion Table 5. Discriminant Validity. In general, the study revealed that the participants showed an PC PU AF BI SI overall positive attitude toward technology, being that each PC 0.87 subscale attained a mean score above the midpoint. The PU 0.22 0.89 attainment of a positive attitude toward technology by the AF 0.07 0.13 0.80 participants in this study could have been developed over BI 0.13 −0.04 0.08 0.74 time, due to government efforts to install technological cul- SI 0.15 −0.05 0.06 0.05 0.88 ture among the citizens, through various initiatives such as Note. Bold values are square roots of AVE from observed variables. “Access to Information (A21),” for over a decade, to enhance Non-bold values are correlations between constructs. PC = perceived technology use at various levels of education in Bangladesh control; PU = perceived usefulness; AF = affective; BI = behavioral intention; (Mou, 2016). There are also several projects and programs SI = social influence. that are geared toward training teachers and trainee-teachers on how to integrate technology into classroom instruction (Karim et al., 2017; Shohel & Kirkwood, 2012; Shohel & also revealed that, in technological abilities and perceived Power, 2010). ease of technology use, males rated themselves in different On the overall attitude toward technology, the current ways significantly, with males rated higher than females research does not reveal a significant variation between (Teo, 2014). A recent study on library and information sci- male and female participants. This contradicts earlier stud- ence students in Bangladesh also reported males with better ies that reported significant differences in attitudes toward technology skills than females (M. A. Hossain & Sormunen, technology between males and females (Padilla-Meléndez 2019). The absence of significant variation between males’ et al., 2013; Teo et al., 2015; Tezci, 2011). Other studies and females’ attitudes toward technology in this study is 10 SAGE Open consistent with studies that reported non-significant gender spurs a positive attitude toward technology. The higher the variation in attitudes toward technology (Bakr, 2011; Wong number of years of technology use, the higher the level of et al., 2012). For example, Bindu (2017) in a study on atti- confidence of the user. This signifies that technology use tude toward ICT among Indian teachers reported non- over time increases the level of confidence of the user, significant differences by gender. Hrtovnová et al. (2015), thereby resulting in a more positive attitude toward technol- also reported gender among several factors that had no sta- ogy. This finding corroborated the result of previous research tistically significant impact on technology acceptance by conducted by Wei et al (2016). More so, Teo (2008) claimed teachers. that one’s more frequent use of technology leads to one’s The positive attitude of females in this study could be attainment of varied technological skills, thereby promoting spurred by the social influence factor, being that the result in one’s overall knowledge of technology. This widens one’s this study shows a significant variation by gender in the way learning potential and prospects, which will consequently participants feel about societal encouragement on technol- promote a positive feeling toward technology. ogy use, with females being more influenced to use technol- Another major aim of the research was to investigate ogy by their social circle than males. This development whether social norms influence trainee-teachers attitudes according to Teo (2008), could be attributed to the changing toward technology, which was not included in previous socialization of females in today’s digital age, and the studies that employ CAS (Grover, 2016; Teo, 2008; Tezci, urgency to have a sense of belonging. This could eventually 2011). The findings of this research discovered that social lessen the barriers to technology acceptance among females. influence has contributed toward shaping the attitudes of Participants from different engineering specializations trainee-teachers toward technology use, especially among varied in their perception of affect, perceived usefulness, and the female participants. The result of the factor analysis also perceived control components. This variation could result indicates that social influence accounts for 9.48% of the from the fact that, in each specialization, there are exclusive variance, from the five factors extracted. It is therefore applications that may dictate how the participants feel about important to consider this additional factor when conducting the technology user interface, and thus result in varied per- studies with CAS. ceptions of the three components which are directly con- nected to the real use of technology. However, their Implications perception of behavioral intention and social influence was the same. This can be understood from the fact that behav- This study provides useful knowledge toward theory, prac- ioral intentional, as well as a social influence, are external to tice, and existing literature. More specifically, the study, the real use of the technology. Among all specializations, theoretically, contributed to research practice in technology participants from civil engineering and computer science and acceptance, by extending the computer attitude scale (CAS), engineering liked and perceived technology as useful for with social influence as an additional important factor to be their daily tasks more than participants from mechanical considered when conducting future research. Therefore, an engineering and electrical and electronic engineering. The extended computer attitude scale (CAS) is established for result also shows that participants from civil engineering and exploring newer research in this domain. In practice, the computer science engineering possessed the required skills findings of this study may serve as an insight into the prog- to use technology without the need for support, more than ress made so far, against the goals that are set to be achieved participants from mechanical engineering and electrical and in technology integration, especially in tertiary engineering electronic engineering. education, where technology impact both pedagogical prac- The differences found among students in different subject tice and professional practice of the learners as well. For specializations in this study are consistent with previous lit- example, the attainment of an overall positive attitude by erature. For example, Fakomogbon (2014) reported a signifi- both genders in the present study signified the positive cant difference in technology attitudes among secondary impact of government efforts to provide equal access to tech- school teachers with different subject domains. It could be nology for both genders (Z. Hossain et al., 2019). It has also possible that their perceptions were shaped by their job become apparent that societal norms should be one of the expectations (Teo, 2008). Trainee-teachers who expect to use important aspects to be handled for effective technology technology more frequently in their future carrier might have integration in Bangladesh and similar developing countries. perceived technology as more useful, and that they have The research also provides important information for more control over it, relative to those who expect less teacher trainers to consider for the course design of training encounter with technology in their teaching profession. In programs for pre-service teachers. In this way, trainee- general, participants from all subject specializations liked teacher can be best prepared on how to adopt technology in using technology and believed it has a positive impact on their teaching carrier. To existing literature, the study pro- their work. The findings of this research also indicated that vides an insight into a new segment of trainee teachers’ atti- frequent use of technology over time leads to the gradual tudes toward technology (Engineering Education) which has development of confidence by the user, and consequently not been reported in prior literature. Salele and Khan 11 usefulness and subsequently, stimulate their intention to use Limitations it. There was no gender variation in the overall positive atti- In general, there are three basic limitations to the current tude realized among the participants in this study. However, study. First, the collection of data was done through self- the findings from the analysis show that social influence is a reports from trainee-teachers, such that there is the potential vital construct, in addition to the four variables of the com- for self-response bias that may sway the true associations puter attitude scale (CAS), that influences the acceptance between variables, though this is common in all survey and intention to use technology by gender. Females’ attitudes research. To limit this potential bias, a combination of posi- in this regard were more likely swayed by their societal tive and negative items was used in the instrument to ensure norms than males. Further research may be conducted with a that true responses are received. The negative items were larger sample size. A Longitudinal design may also be then reverse coded after data collection to ensure meaningful employed to examine how the trainee teachers’ attitudes and analysis. Secondly, the participants in this study were engi- experiences change over time. While the additional variable neering education trainee-teachers and the sample size was (social influence) could be adopted for conducting other relatively small, and therefore, the generalization of the find- research with the computer attitude scale (CAS), other vari- ings is rather limited. However, the sample size had reached ables such as facilitating conditions and technological com- the minimum threshold recommended for the MLE analysis plexity may also be explored. technique employed in this study (Ding et al., 1995). Moreover, future studies may consider larger sample size. Acknowledgments Thirdly, the variables used in the instrument were basically The authors wish to thank Professor Timothy Teo, Murdoc determined by Computer Attitude Scale (CAS), though one University, Australia for sharing his validated tool. important variable (social norms) was included. However, other significant variables that may influence attitudes Declaration of Conflicting Interests toward technology were excluded and, may consequently The author(s) declared no potential conflicts of interest with respect lead to limited understanding of the trainee-teachers attitude to the research, authorship, and/or publication of this article. toward technology. Other limitations peculiar to the inclu- sion of the social influence component in the study include: Funding the number of male participants was higher than the number The author(s) received no financial support for the research, author- of female participants. This is a general variation in the engi- ship, and/or publication of this article. neering education of Bangladesh where male students enroll- ing in engineering programs always outnumber female ORCID iDs students (Jahan et al., 1998). Furthermore, the significance of social norms on technology acceptance and use obtained Nafiu Salele https://orcid.org/0000-0003-0756-3896 in this study is limited to trainee teachers, and may not be Md. Shahadat Hossain Khan https://orcid.org/0000-0003-0195 -1804 generalized to other groups such as in-service teachers and students, since their perception of the importance of their References social circle may differ from that of trainee teachers. Hence, more validation studies need to be conducted with samples Abbasi, W. T., Ibrahim, A. H., & Ali, F. B. (2021, 25–26 June). from segments other than trainee teachers. Finally, both uni- Perceptions about English as second language teachers’ versities where the study was conducted are located in the technology-based English language teaching in Pakistan: Attitudes, uses of technology and challenges [Conference ses- capital city, and social norms in the rural areas might influ- sion]. 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SAGE OpenSAGE

Published: Jun 20, 2022

Keywords: engineering education; technology use; teachers’; attitudes; trainee-teacher; Computer Attitude Scale

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