1. Engagement can be influenced by interest, as people tend to be more engaged in activities they are interested in; thus, interest is a determinant of engagement.
2. Effort is closely related to interest in the same way: more effort is invested if the person has interest in the activity. The relation between engagement and effort can be resumed by: engagement can be present with or without effort; if the activity is pleasant (and/or easy), engagement is possible without effort; in the case of more unpleasant (and/or difficult) activities, effort may be required to stay engaged.
3. The difference between engagement and focus of attention, as used in research, is that focus of attention refers to attention through a specific sensorial channel (e.g., visual focus), while engagement refers to the entire mental activity (involving at the same time perception, attention, reasoning, volition, and emotions).
4. Engagement is just one aspect indicating that for a reason or another, the person is motivated to do the activity she/he is engaged in, or, on the contrary, if the person is disengaged, that she/he may not be motivated to do the activity. In other words, engagement is an indicator of motivation.
1. Bayesian Nets with K2 algorithm and maximum three parent nodes (BNs).
2. Logistic regression (LR).
3. Simple logistic classification (SL).
4. Instance-based classification with IBk algorithm (IBk).
5. Attribute Selected Classification using J48 classifier and Best First search (ASC).
6. Bagging using REP (reduced error pruning) tree classifier (B).
7. Classification via Regression (CvR).
8. Decision Trees with J48 classifier based on Quilan's C4.5 algorithm [ 21] (DTs).
1. Most pages, i.e., more than 99 percent, require less than 400 seconds to be read. Moreover, 70 percent of the pages require less than 100 seconds and only five pages, i.e., less than 1 percent, are left out.
2. Very few students watched videos (that could be longer than 5 or even 10 minutes, which would considerably affect the way to establish engagement level for a 10-minutes sequence).
3. There may be individual differences in reading speed, and by allowing a rather loose upper threshold, slow speed is taken into account. However, fast speed is not covered.
4. Some learners go through the material more than once, leading to an at least doubled time needed for reading.
M. Cocea is with the London Knowledge Lab, Department of Computer Science and Information Systems, Birkbeck College, University of London, 23-29 Emerald Street, WC1N 3QS London. E-mail: email@example.com.
S. Weibelzahl is with the School of Computing, National College of Ireland, Mayor Street, IFSC, Dublin 1, Ireland. E-mail: firstname.lastname@example.org.
Manuscript received 13 Nov. 2009; revised 31 May 2010; accepted 2 June 2010; published online 14 July 2010.
For information on obtaining reprints of this article, please send e-mail to: email@example.com, and reference IEEECS Log Number TLT-2009-11-0152.
Digital Object Identifier no. 10.1109/TLT.2010.14.
Mihaela Cocea received the BSc degree in psychology and education and the BSc degree in computer science from “Al. I. Cuza” University of Iasi in 2002 and 2003, respectively, and the MSc degree in human relations and communication by Research in Learning Technologies from the National College of Ireland in 2007. She is currently working toward the PhD degree at the Department of Computer Science and Information Systems, Birkbeck College, University of London. Her research interests include intelligent learning environments, user modeling, and adaptive feedback.
Stephan Weibelzahl received the PhD degree from the University of Trier, Germany. He holds a lecturer position at the National College of Ireland in Dublin. After heading a research group at the Fraunhofer Institute of Experimental Software Engineering (IESE), Kaiserslautern, Germany, he joined National College of Ireland in 2004. With his background in psychology and computer science, he has long-standing research expertise in developing and evaluating Adaptive e-Learning Systems. His research interests include Adaptive Systems, learning technologies, evaluation, Knowledge Management, and Blended Learning.