Towards an Online Continuous Adaptation Mechanism (OCAM) for Enhanced Engagement: An EEG Study

ABSTRACT Individual preferences for learning environments can be linked to a specific behavior. The tendency of such behavior can somehow be associated with an individual’s ability to cognitively engage in the learning process without being distracted by other stimuli. An online continuous adaptation mechanism (OCAM) of learning contents was developed in order to regulate the presentation of learning contents based on changes in the learner’s aptitude level. This was claimed to stimulate a better cognitive and emotional response among learners, thus stimulating their engagement. A total of 41 students (36 male and 5 female; age 20–25 years) participated in this study. The results revealed that learners’ levels of concentration and cognitive load were positively influenced by the OCAM, which significantly increased their engagement. Our findings can be used to inform designers and developers of online learning systems about the importance of regulating the presentation of learning contents according to the aptitude level of individual learners. The proposed OCAM can improve learners’ ability to process specific information meaningfully and make the inferences necessary for understanding the learning content.

[1]  Michel Janosz,et al.  Part IV commentary: Outcomes of engagement and engagement as an outcome: Some consensus, divergences, and unanswered questions. , 2012 .

[2]  Sharon K Tindall-Ford,et al.  When two sensory modes are better than one , 1997 .

[3]  Robert Glaser,et al.  Higher Cognitive Goals for Education: An Introduction , 2013 .

[4]  John H. Holland,et al.  Studying Complex Adaptive Systems , 2006, J. Syst. Sci. Complex..

[5]  Olga C. Santos,et al.  Emotions and Personality in Adaptive e-Learning Systems: An Affective Computing Perspective , 2017, Emotions and Personality in Personalized Services.

[6]  Elvira Popescu,et al.  Adaptation provisioning with respect to learning styles in a Web-based educational system: an experimental study , 2010, J. Comput. Assist. Learn..

[7]  M. Chi,et al.  The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes , 2014 .

[8]  Ping Zhang,et al.  A person-artefact-task (PAT) model of flow antecedents in computer-mediated environments , 2003, Int. J. Hum. Comput. Stud..

[9]  James J. Appleton,et al.  Engagement as flourishing: The contribution of positive emotions and coping to adolescents' engagement at school and with learning , 2008 .

[10]  Charalambos Vrasidas Issues of pedagogy and design in e-learning systems , 2004, SAC '04.

[11]  Mary H. MacLean,et al.  Resting EEG in alpha and beta bands predicts individual differences in attentional blink magnitude , 2012, Brain and Cognition.

[12]  Charles D. Dziuban,et al.  Adaptive Learning in Psychology: Wayfinding in the Digital Age , 2016 .

[13]  R. Kristeva-Feige,et al.  Effects of attention and precision of exerted force on beta range EEG-EMG synchronization during a maintained motor contraction task , 2002, Clinical Neurophysiology.

[14]  Ray-I Chang,et al.  Hybrid learning style identification and developing adaptive problem-solving learning activities , 2016, Comput. Hum. Behav..

[15]  Chin-Lung Hsu,et al.  Why do people play on-line games? An extended TAM with social influences and flow experience , 2004, Inf. Manag..

[16]  Michelle N. Lumicao,et al.  EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. , 2007, Aviation, space, and environmental medicine.

[17]  Jacquelynn A. Malloy,et al.  Students’ Engagement in Literacy Tasks , 2015 .

[18]  Jennifer L. Cuzzocreo,et al.  Effect of handedness on fMRI activation in the medial temporal lobe during an auditory verbal memory task , 2009, Human brain mapping.

[19]  J. McCain,et al.  Need for cognition is related to higher general intelligence, fluid intelligence, and crystallized intelligence, but not working memory , 2013 .

[20]  Eunju Lee,et al.  The Relationship of Motivation and Flow Experience to Academic Procrastination in University Students , 2005, The Journal of genetic psychology.

[21]  Ok-Choon Park,et al.  Adaptive Instructional Systems , 2007 .

[22]  Beate List,et al.  An evaluation of open source e-learning platforms stressing adaptation issues , 2005, Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05).

[23]  M Doppelmayr,et al.  Theta synchronization in the human EEG and episodic retrieval , 1998, Neuroscience Letters.

[24]  Yiannis Gabriel,et al.  Emotion, learning and organizational change: Towards an integration of psychoanalytic and other perspectives , 2001 .

[25]  Hosam Al-Samarraie,et al.  Towards incorporating personality into the design of an interface: a method for facilitating users’ interaction with the display , 2018, User Modeling and User-Adapted Interaction.

[26]  D. R. Paulson,et al.  Active Learning in the College Classroom. , 1998 .

[27]  M. Csíkszentmihályi,et al.  The Dynamics of Intrinsic Motivation: A Study of Adolescents , 2014 .

[28]  Barry Harper,et al.  Handbook of Research on Learning Design and Learning Objects: Issues, Applications and Technologies , 2008 .

[29]  A. Demetriou,et al.  Intelligence, Mind, and Reasoning Structure and Development , 1994 .

[30]  Thomas L. Good,et al.  The Classroom Ratio of High- and Low-aptitude Students and Its Effect on Achievement , 1981 .

[31]  V. Shute,et al.  Cognitive Approaches To Automated Instruction , 1992 .

[32]  C. Mega,et al.  What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. , 2014 .

[33]  S. Steinhauer,et al.  Blink before and after you think: blinks occur prior to and following cognitive load indexed by pupillary responses. , 2008, Psychophysiology.

[34]  Jennifer A. Fredricks,et al.  School Engagement: Potential of the Concept, State of the Evidence , 2004 .

[35]  Jacqueline Bourdeau,et al.  Advances in Intelligent Tutoring Systems , 2010 .

[36]  Carlos De Backer,et al.  The design and pilot evaluation of an interactive learning environment for introductory programming influenced by cognitive load theory and constructivism , 2013, Comput. Educ..

[37]  J. Gustafsson,et al.  General and Specific Abilities as Predictors of School Achievement. , 1993, Multivariate behavioral research.

[38]  Namin Shin,et al.  Online learner's 'flow' experience: an empirical study , 2006, Br. J. Educ. Technol..

[39]  G. Pfurtscheller,et al.  Event-related cortical desynchronization detected by power measurements of scalp EEG. , 1977, Electroencephalography and clinical neurophysiology.

[40]  S. Derry,et al.  Learning from Examples: Instructional Principles from the Worked Examples Research , 2000 .

[41]  Kevin J. Pugh,et al.  Motivational Influences on Transfer , 2006 .

[42]  Paul van Schaik,et al.  Measuring flow experience in an immersive virtual environment for collaborative learning , 2012, J. Comput. Assist. Learn..

[43]  René J. Huster,et al.  EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial , 2017, Front. Hum. Neurosci..

[44]  E. Basar,et al.  Oscillatory brain theory: a new trend in neuroscience. , 1999, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[45]  Rafael Ramírez,et al.  Detecting Emotion from EEG Signals Using the Emotive Epoc Device , 2012, Brain Informatics.

[46]  H. Swanson Influence of Metacognitive Knowledge and Aptitude on Problem Solving. , 1990 .

[47]  Anne G E Collins,et al.  How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis , 2012, The European journal of neuroscience.

[48]  D. Schunk,et al.  Academic self-efficacy. , 2014 .

[49]  Mehmet Göktürk,et al.  Psychophysiological measures of human cognitive states applied in human computer interaction , 2011, WCIT.

[50]  Slava Kalyuga,et al.  Student Perceptions and Cognitive Load: What Can They Tell Us about e-Learning Web 2.0 Course Design? , 2009 .

[51]  S. Kozlowski,et al.  Active learning: effects of core training design elements on self-regulatory processes, learning, and adaptability. , 2008, The Journal of applied psychology.

[52]  Celia Popovic,et al.  Teaching for quality learning at university. (2nd Edn.) , 2013 .

[53]  A. H. Church,et al.  The Pearls and Perils of Identifying Potential , 2009, Industrial and Organizational Psychology.

[54]  Hosam Al-Samarraie,et al.  A First Look at the Effectiveness of Personality Dimensions in Promoting Users’ Satisfaction With the System , 2018 .

[55]  Johanna Leppävirta,et al.  Complex Problem Exercises in Developing Engineering Students' Conceptual and Procedural Knowledge of Electromagnetics , 2011, IEEE Transactions on Education.

[56]  Kyungeun Cho,et al.  A Development Architecture for Serious Games Using BCI (Brain Computer Interface) Sensors , 2012, Sensors.

[57]  Gavriel Salvendy,et al.  Analytic Cognitive Task Allocation: a decision model for cognitive task allocation , 2008 .

[58]  G. Boulton‐Lewis Teaching for quality learning at university , 2008 .

[59]  J. Sweller,et al.  Cognitive Load Theory and Complex Learning: Recent Developments and Future Directions , 2005 .

[60]  Jose Benitez-Amado,et al.  Managerial perceptions of the competitive environment and dynamic capabilities generation , 2010, Ind. Manag. Data Syst..

[61]  B. Homer,et al.  Optimizing cognitive load for learning from computer-based science simulations , 2006 .

[62]  M. Csíkszentmihályi,et al.  Optimal experience: Psychological studies of flow in consciousness. , 1988 .

[63]  Udo Konradt,et al.  Flow experience and positive affect during hypermedia learning , 2003, Br. J. Educ. Technol..

[64]  A. Alzahrani,et al.  E-learning continuance satisfaction in higher education: a unified perspective from instructors and students , 2018 .

[65]  L. Kyriakides,et al.  Using a multidimensional approach to measure the impact of classroom-level factors upon student achievement: a study testing the validity of the dynamic model , 2008 .

[66]  Claude Frasson,et al.  Managing Learner's Affective States in Intelligent Tutoring Systems , 2010, Advances in Intelligent Tutoring Systems.

[67]  BudimacZoran,et al.  E-Learning personalization based on hybrid recommendation strategy and learning style identification , 2011 .

[68]  Kay Wijekumar,et al.  Interrupted cognition in an undergraduate programming course , 2006, ASIST.

[69]  Carlos M. Gómez,et al.  Temporal evolution of α and β bands during visual spatial attention , 2001 .

[70]  Alexandros Paramythis,et al.  Adaptive Learning Environments and e-Learning Standards. , 2004 .

[71]  F. Freeman,et al.  Evaluation of an adaptive automation system using three EEG indices with a visual tracking task , 1999, Biological Psychology.

[72]  Brendon Towle,et al.  Designing Adaptive Learning Environments with Learning Design , 2005 .

[73]  L. Thompson,et al.  Predicting academic achievement with cognitive ability , 2007 .

[74]  Steven E. Stemler What Should University Admissions Tests Predict? , 2012 .

[75]  Timothy Teo,et al.  Can structured representation enhance students' thinking skills for better understanding of E-learning content? , 2013, Comput. Educ..

[76]  Danny Weyns,et al.  Keep it SIMPLEX: satisfying multiple goals with guarantees in control-based self-adaptive systems , 2016, SIGSOFT FSE.

[77]  Irene W. Y. Ma,et al.  Emotion, cognitive load and learning outcomes during simulation training , 2012, Medical education.

[78]  Mimi Bong,et al.  Role of Self-Efficacy and Task-Value in Predicting College Students' Course Performance and Future Enrollment Intentions. , 2001, Contemporary educational psychology.

[79]  Elizabeth O. Hayward,et al.  Emotional design in multimedia learning: Effects of shape and color on affect and learning , 2014 .

[80]  P. Armstrong,et al.  Individuals and environments: Linking ability and skill ratings with interests. , 2010, Journal of counseling psychology.

[81]  Su-Houn Liu,et al.  APPLYING THE TECHNOLOGY ACCEPTANCE MODEL AND FLOW THEORY TO ONLINE E-LEARNING USERS' ACCEPTANCE BEHAVIOR , 2005 .

[82]  Ming-Chi Lee,et al.  Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model , 2010, Comput. Educ..

[83]  Arthur C. Graesser,et al.  What Works: Creating Adaptive and Intelligent Systems for Collaborative Learning Support , 2014, Intelligent Tutoring Systems.

[84]  Roberto Malinow,et al.  Emotion Enhances Learning via Norepinephrine Regulation of AMPA-Receptor Trafficking , 2007, Cell.

[85]  Iain Reid,et al.  Understanding student engagement in online learning environments: the role of reflexivity , 2017 .

[86]  J. Kottke Additional evidence for the short form of the Universality-Diversity Scale , 2011 .

[87]  Stefanos D. Kollias,et al.  An intelligent e-learning system based on learner profiling and learning resources adaptation , 2008, Comput. Educ..

[88]  M. Csíkszentmihályi,et al.  The Concept of Flow , 2014 .

[89]  V. Aleven,et al.  Help Seeking and Help Design in Interactive Learning Environments , 2003 .

[90]  Pierre Dillenbourg Some technical implications of distributed cognition on the design of interactive learning environments , 1996 .

[91]  K. Stanovich,et al.  The Cognitive Reflection Test as a predictor of performance on heuristics-and-biases tasks , 2011, Memory & cognition.

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

[93]  Martin E. P. Seligman,et al.  Positive education: positive psychology and classroom interventions , 2009 .

[94]  Richard E. Snow,et al.  Aptitude Theory: Yesterday, Today, and Tomorrow , 1992 .

[95]  Jodi Asbell-Clarke,et al.  Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning , 2016, Comput. Hum. Behav..

[96]  Jennifer C. Richardson,et al.  The Role of Students' Cognitive Engagement in Online Learning , 2006 .

[97]  J. Pascual-Leone Can we model organismic causes of working memory, efficiency and fluid intelligence? A meta-subjective perspective , 2013 .

[98]  H. Schmidt,et al.  The role of teachers in facilitating situational interest in an active-learning classroom , 2011 .

[99]  Andrew J. Martin Part II Commentary: Motivation and Engagement: Conceptual, Operational, and Empirical Clarity , 2012 .

[100]  Keith W. Brawner,et al.  Efficacy of Measuring Engagement during Computer-Based Training with Low-Cost Electroencephalogram (EEG) Sensor Outputs , 2012 .

[101]  Michael C. Pyryt Human cognitive abilities: A survey of factor analytic studies , 1998 .

[102]  Karen Becker Individual and organisational unlearning: directions for future research , 2005 .

[103]  D. Leutner,et al.  Direct Measurement of Cognitive Load in Multimedia Learning , 2003 .

[104]  V. Shute,et al.  Adaptive E-Learning , 2003, Educational Psychologist.

[105]  A. Pope,et al.  Biocybernetic system evaluates indices of operator engagement in automated task , 1995, Biological Psychology.

[106]  Martin A. Andresen Asynchronous Discussion Forums: Success Factors, Outcomes, Assessments, and Limitations , 2009, J. Educ. Technol. Soc..

[107]  J. Sweller COGNITIVE LOAD THEORY, LEARNING DIFFICULTY, AND INSTRUCTIONAL DESIGN , 1994 .

[108]  K. Scherer What are emotions? And how can they be measured? , 2005 .

[109]  Randall W. Engle,et al.  Faster, smarter? Working memory capacity and perceptual speed in relation to fluid intelligence , 2012 .

[110]  Paul A. Kirschner,et al.  Cognitive load theory: implications of cognitive load theory on the design of learning , 2002 .

[111]  Liesbeth Kester,et al.  Cognitive load theory and multimedia learning, task characteristics and learning engagement: The Current State of the Art , 2011, Comput. Hum. Behav..

[112]  Lisa Linnenbrink-Garcia,et al.  Academic Emotions and Student Engagement , 2012 .

[113]  Syh-Jong Jang,et al.  A Theoretical and Methodological Approach to Examine Young Learners’ Cognitive Engagement in Science Learning , 2013 .

[114]  L. Leach,et al.  Linking academic emotions and student engagement: mature-aged distance students’ transition to university , 2015 .

[115]  Zoran Budimac,et al.  E-Learning personalization based on hybrid recommendation strategy and learning style identification , 2011, Comput. Educ..

[116]  Pavlo D. Antonenko,et al.  Using Electroencephalography to Measure Cognitive Load , 2010 .