Assessment of Motivation in Online Learning Environments

This research outline refers to the assessment of motivation in online learning environments. It includes a presentation of previous approaches, most of them based on Keller’s ARCS model, and argues for an approach based on Social Cognitive Learning Theory, in particular building on self-efficacy and self-regulation concepts. The research plan includes two steps: first, detect the learners in danger of dropping-out based on their interaction with the system; second, create a model of the learner’s motivation (including self-efficacy, self-regulation, goal orientation, attribution and perceived task characteristics) upon which intervention can be done.

[1]  C. K. Lim.  Computer self‐efficacy, academic self‐concept, and other predictors of satisfaction and future participation of adult distance learners , 2001 .

[2]  M. Csíkszentmihályi.  Finding Flow: The Psychology of Engagement with Everyday Life , 1997 .

[3]  Joseph E. Beck,et al.  Engagement tracing: using response times to model student disengagement , 2005, AIED.

[4]  Ulf-Dietrich Reips.  Standards for Internet-based experimenting. , 2002, Experimental psychology.

[5]  F. Heider.  The psychology of interpersonal relations , 1958 .

[6]  M. Driscoll.  Psychology of Learning for Instruction , 1993 .

[7]  A. Bandura.  Social learning theory , 1977 .

[8]  P. Pintrich,et al.  Motivation in Education: Theory, Research, and Applications , 1995 .

[9]  H. Deeg,et al.  Instituto de Astrofísica de Canarias (Spain) , 2022 .

[10]  M. Mahoney,et al.  Self-control: power to the person , 1974 .

[11]  Mihaela Cocea,et al.  Eliciting Motivation Knowledge from Log Files Towards Motivation Diagnosis for Adaptive Systems , 2007, User Modeling.

[12]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[13]  B. Zimmerman,et al.  Self-regulation of learning and performance: Issues and educational applications. , 1994 .

[14]  E. Witta,et al.  Online Students' Perceived Self-Efficacy: Does It Change?. , 2001 .

[15]  Ian H. Witten,et al.  Weka: Practical machine learning tools and techniques with Java implementations , 1999 .

[16]  Stephan Weibelzahl,et al.  Developing Adaptive Internet Based Courses with the Authoring System NetCoach , 2001, OHS-7/SC-3/AH-3.

[17]  Karsten Rex,et al.  The Relationship of Computer Experience and Computer Self-Efficacy to Performance in Introductory Computer Literacy Courses , 1998 .

[18]  D. Schunk.  Goal Setting and Self-Efficacy During Self-Regulated Learning , 1990 .

[19]  Klaus Krippendorff,et al.  Answering the Call for a Standard Reliability Measure for Coding Data , 2007 .

[20]  L. Rips,et al.  The Psychology of Survey Response , 2000 .

[21]  B. Zimmerman.  Attaining self-regulation: A social cognitive perspective. , 2000 .

[22]  Päivi Häkkinen,et al.  Changes in Computer Anxiety in a Required Computer Course , 1994 .

[23]  Ryan Shaun Joazeiro de Baker,et al.  Detecting Student Misuse of Intelligent Tutoring Systems , 2004, Intelligent Tutoring Systems.

[24]  P. Karoly.  Mechanisms of Self-Regulation: A Systems View , 1993 .

[25]  Mihaela Cocea,et al.  Can Log Files Analysis Estimate Learners' Level of Motivation? , 2006, LWA.

[26]  José Justo Megías Quirós.  Sociedad de la información , 2002 .

[27]  Wilhelmina C. Savenye,et al.  Applying Social Cognitive Constructs of Motivation To Enhance Student Success in Online Distance Education. , 2003 .

[28]  A. Bandura.  Growing primacy of human agency in adaptation and change in the electronic era , 2002 .

[29]  Neil T. Heffernan,et al.  Detection and Analysis of Off-Task Gaming Behavior in Intelligent Tutoring Systems , 2006, Intelligent Tutoring Systems.

[30]  Paivi Kolehmainen.  The Changes in Computer Anxiety in a Required Computer Course. , 1992 .

[31]  Barry Smyth,et al.  Adaptive Hypermedia and Adaptive Web-Based Systems, 4th International Conference, AH 2006, Dublin, Ireland, June 21-23, 2006, Proceedings , 2006, AH.

[32]  Beverly Park Woolf,et al.  A Dynamic Mixture Model to Detect Student Motivation and Proficiency , 2006, AAAI.

[33]  Roberta M. Roth,et al.  Computer Self-Efficacy: A Practical Indicator of Student Computer Competency in Introductory IS Courses , 1998, Informing Sci. Int. J. an Emerg. Transdiscipl..

[34]  B. Weiner.  Achievement Motivation and Attribution Theory , 1974 .

[35]  John M. Keller,et al.  Motivation and instructional design: A theoretical perspective , 1979 .

[36]  J. Rotter.  Generalized expectancies for internal versus external control of reinforcement. , 1966, Psychological monographs.

[37]  Carole A. Ames.  Classrooms: Goals, structures, and student motivation. , 1992 .

[38]  Scotty D. Craig,et al.  Integrating Affect Sensors in an Intelligent Tutoring System , 2004 .

[39]  Charles B. Hodges.  Designing to Motivate: Motivational Techniques to Incorporate in E-Learning Experiences , 2004 .

[40]  M. DeTure,et al.  Cognitive Style and Self-Efficacy: Predicting Student Success in Online Distance Education , 2004 .

[41]  Helen Pain,et al.  Informing the Detection of the Students' Motivational State: An Empirical Study , 2002, Intelligent Tutoring Systems.

[42]  Robert Irizarry,et al.  Self-Efficacy & Motivation Effects on Online Psychology Student Retention. , 2002 .

[43]  Marcy P. Driscoll,et al.  Psychology of Learning for Instruction (3rd ed. , 2005 .

[44]  A. Bandura.  Self-efficacy mechanism in human agency , 2024, Psihologìâ ì suspìlʹstvo.

[45]  Mihaela Cocea,et al.  Cross-System Validation of Engagement Prediction from Log Files , 2007, EC-TEL.

[46]  D. Dillman.  Mail and internet surveys: The tailored design method, 2nd ed. , 2007 .

[47]  Carlo Strapparava,et al.  Adaptive Hypermedia and Adaptive Web-Based Systems, 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008. Proceedings , 2008, AH.

[48]  Beverly Park Woolf,et al.  Inferring learning and attitudes from a Bayesian Network of log file data , 2005, AIED.

[49]  Carole A. Ames.  Achievement goals and the classroom motivational climate. , 1992 .

[50]  B. Zimmerman.  A social cognitive view of self-regulated academic learning. , 1989 .

[51]  B. Zimmerman,et al.  Student Differences in Self-Regulated Learning: Relating Grade, Sex, and Giftedness to Self-Efficacy and Strategy Use. , 1990 .

[52]  Rosalind W. Picard,et al.  Classical and novel discriminant features for affect recognition from speech , 2005, INTERSPEECH.

[53]  Peggy A. Ertmer,et al.  Enhancing self-efficacy for computer technologies through the use of positive classroom experiences , 1994 .

[54]  D. McInerney,et al.  Student Teachers, Computer Anxiety and Computer Experience , 1994 .

[55]  B. Zimmerman.  Self-regulating academic learning and achievement: The emergence of a social cognitive perspective , 1990 .

[56]  Alvin Y. Wang,et al.  Predictors of web-student performance: the role of self-efficacy and reasons for taking an on-line class , 2002, Comput. Hum. Behav..

[57]  B. Shneiderman,et al.  Windows of opportunity in electronic classrooms , 1995, CACM.

[58]  Cheryl Campanella Bracken,et al.  Practical Resources for Assessing and Reporting Intercoder Reliability in Content Analysis Research Projects , 2005 .

[59]  W. Michael Reed,et al.  The effects of prior experience and instructional format on teacher education students' computer anxiety and performance , 1993 .

[60]  Mihaela Cocea.  Extendibility of educational systems to include a learner-adaptive motivational module , 2006 .

[61]  Frederick Burns King.  Asynchronous distance education courses employing web-based instruction : implications of individual study skills self-efficacy and self-regulated learning , 2001 .

[62]  Lei Qu,et al.  Classifying Learner Engagement through Integration of Multiple Data Sources , 2006, AAAI.

[63]  A. Bandura,et al.  Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. , 1981 .

[64]  Ning Wang,et al.  Using Learner Focus of Attention to Detect Learner Motivation Factors , 2005, User Modeling.

[65]  Eric M. Anderman,et al.  Social Predictors of Changes in Students' Achievement Goal Orientations. , 1999, Contemporary educational psychology.

[66]  Neil T. Heffernan,et al.  Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems , 2006, Intelligent Tutoring Systems.

[67]  Mihaela Cocea,et al.  Learning Engagement: What Actions of Learners Could Best Predict It? , 2007, AIED.

[68]  Kathleen Holt,et al.  A pattern analysis of students' achievement goals. , 1993 .

[69]  Scott W. Brown,et al.  Student Traits and Attributes Contributing to Success in Online Courses: Evaluation of University Online Courses , 2004 .

[70]  Mihaela Cocea,et al.  Motivation: included or excluded from E-learning , 2006 .

[71]  J. Keller.  Development and use of the ARCS model of instructional design , 1987 .

[72]  Ian Witten,et al.  Data Mining , 2000 .

[73]  A. Bandura,et al.  Self-evaluative and self-efficacy mechanisms governing the motivational effects of goal systems. , 1983 .

[74]  A. Bandura.  Social Foundations of Thought and Action: A Social Cognitive Theory , 1985 .

[75]  Yuri Ivanov,et al.  Probabilistic combination of multiple modalities to detect interest , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[76]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[77]  T. Busch.  Gender Differences in Self-Efficacy and Attitudes toward Computers , 1995 .

[78]  W. Lewis Johnson,et al.  Detecting the Learner's Motivational States in An Interactive Learning Environment , 2005, AIED.