Access the full text.
Sign up today, get DeepDyve free for 14 days.
B Little (2001)
Achieving high performance through e-learningInd Commer Train, 33
A Heinze, C Procter, B Scott (2007)
Use of conversation theory to underpin blended learningInt J Teach Case Stud, 1
Z Ma (2006)
Web-based intelligent e-learning systems
D Palmer-Brown, C Jayne (2011)
Snap-drift neural network for self-organisation and sequence learningNeural Netw, 24
A Rane, M Sasikumar (2007)
Innovations in e-learning, instruction technology, assessment, and engineering education
P Bang (2003)
Language learning online: towards best practice
M Alessi, S Trollip (2001)
Multimedia for learning: methods and development
N Özdener, HM Satar (2009)
Effectiveness of various oral feedback techniques in CALL vocabulary learning materialsEgitim Arastirmalari—Eurasian J Educ Res, 34
P Race, S Brown (2005)
500 tips for tutors
ML Epstein, AD Lazarus, TB Calvano, KA Mathews, RA Hendel, BB Epstein, GM Brosvic (2002)
Immediate feedback assessment technique promotes learning and corrects inaccurate first responsePsychol Record, 52
BJ Reiser, DY Kimberg (1992)
Computer assisted instruction and intelligent tutoring systems
RC Clark, RE Mayer (2009)
Handbook of improving performance in the workplace: instructional design and training delivery
M Paxton (2000)
A linguistic perspective on multiple-choice questioning assessment and evaluationAssess Eval High Educ, 25
MM Nelson, CD Schunn (2009)
The nature of feedback: how different types of peer feedback affect writing performanceInstr Sci, 37
PR Garber (2004)
Giving and receiving performance feedback
P Race (2006)
The lecturer’s toolkit—a practical guide to assessment. Learning and teaching
JLF Alemán, D Palmer-Brown, C Jayne (2011)
Effects of response-driven feedback in computer science learningIEEE Trans Educ, 54
WL Kuechler, MG Simkin (2003)
How well do multiple choice tests evaluate student understanding in computer programming classes?J Inf Syst Educ, 14
When students attempt multiple-choice questions (MCQs) they generate invaluable information which can form the basis for understanding their learning behaviours. In this research, the information is collected and automatically analysed to provide customized, diagnostic feedback to support students’ learning. This is achieved within a web-based system, incorporating the snap-drift neural network based analysis of students’ responses to MCQs. This paper presents the results of a large trial of the method and the system which demonstrates the effectiveness of the feedback in guiding students towards a better understanding of particular concepts.
Artificial Intelligence Review – Springer Journals
Published: Aug 9, 2013
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.