A study of the impact of task complexity and interface design on e-learning task adaptations

E-learners use different strategies of learning and interactions in different learning situations. The learning task's complexity and the design of user interface used together influences learner's adaptations in their interaction tasks. This research studies influence of task's complexity and interface design, on learner's adaptations in interaction tasks. The study reveals learner's interaction behavior in situations of changing cognitive demands of learning tasks. The participants of the study solved learning tests using an e-learning web application with two distinct types of graphical user interfaces (GUI-1 and GUI-2). GUI-1 had hierarchical navigation design while GUI-2 had non-hierarchical design. Different sample groups (K, C and A) were administered learning tests having different complexities such as knowledge based (K), comprehension based (C) and application based (A). The interaction tasks such as total pages visited (Tpv) and total operations done (Top) during the learning tests were recorded for computing task adaptation score (TAS). The adaptation scores for GUI-1 and GUI-2 in various sample groups (K, C and A) were compared and analyzed. The study concludes that the hierarchical or non-hierarchical navigation designs have no significant effect on learner's adaptations in Tpv and Top. However learning test complexity (knowledge, comprehension and application) significantly affects task adaptation scores.

[1]  Kazuhiro Ueda,et al.  Evaluation of Users' Adaptation by Applying LZW Compression Algorithm to Operation Logs , 2004, KES.

[2]  Qingtian Zeng,et al.  Mining User's Interest from Reading Behavior in E-learning System , 2007, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007).

[3]  Shailey Minocha,et al.  Combining Eye Tracking and Conventional Techniques for Indications of User-Adaptability , 2005, INTERACT.

[4]  Alan R. Dennis,et al.  Does fit matter? the impact of fit on collaboration technology effectiveness over time , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[5]  Francesc Santanach Delisau,et al.  Capturing User Behavior in e-Learning Environments , 2007, WEBIST.

[6]  James T. C. Teng,et al.  Exploring Technology and Task Adaptation Among Individual Users of Mobile Technology , 2010, ICIS.

[7]  Arh Arnout Fischer,et al.  User adaptation in user-system-interaction , 2004 .

[8]  Stephen J. Payne,et al.  Adaptive browsing: Sensitivity to time pressure and task difficulty , 2012, Int. J. Hum. Comput. Stud..

[9]  Joachim Meyer,et al.  Benefits and costs of adaptive user interfaces , 2010, Int. J. Hum. Comput. Stud..

[10]  Enric Mor,et al.  E-learning personalization based on itineraries and long-term navigational behavior , 2004, WWW Alt. '04.

[11]  Wayne D. Gray,et al.  Adapting to the task environment: Explorations in expected value , 2005, Cognitive Systems Research.

[12]  Panagiotis Germanakos,et al.  Eye-Tracking Users' Behavior in Relation to Cognitive Style within an E-learning Environment , 2009, 2009 Ninth IEEE International Conference on Advanced Learning Technologies.

[13]  Carlos Delgado Kloos,et al.  Student Behavior and Interaction Patterns With an LMS as Motivation Predictors in E-Learning Settings , 2010, IEEE Transactions on Education.

[14]  Rainer Bromme,et al.  Is adaptation to task complexity really beneficial for performance , 2012 .

[15]  Enric Mor,et al.  User navigational behavior in e-learning virtual environments , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).

[16]  James G. Barber E-learning: Supplementary or disruptive? , 2013 .

[17]  J. Merriënboer,et al.  Selecting learning tasks: Effects of adaptation and shared control on learning efficiency and task involvement☆ , 2008 .

[18]  Feng Wang,et al.  E-learning Behavior Analysis Based on Fuzzy Clustering , 2009, 2009 Third International Conference on Genetic and Evolutionary Computing.

[19]  Catherine L. Smith,et al.  User adaptation: good results from poor systems , 2008, SIGIR '08.

[20]  Karen Bird An 'objective- centred' approach to course redesign: using learning objectives to integrate e-learning , 2008 .

[21]  Toni Noble,et al.  Integrating the Revised Bloom's Taxonomy with Multiple Intelligences: A Planning Tool for Curriculum Differentiation , 2004, Teachers College Record: The Voice of Scholarship in Education.

[22]  Slava Kalyuga,et al.  Enhancing Instructional Efficiency of Interactive E-learning Environments: A Cognitive Load Perspective , 2007 .

[23]  Heshan Sun,et al.  Adaptive System Use; An Investigation at the System Feature Level , 2008, ICIS.

[24]  Anne Beaudry,et al.  Understanding User Responses to Information Technology: A Coping Model of User Adaption , 2005, MIS Q..

[25]  Yueh-Min Huang,et al.  Real-time learning behavior mining for e-learning , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).

[26]  Jing-Shiuan Hua,et al.  Discovery of Educational Objective on E-Learning Resource: A Competency Approach , 2007, ICWL.