Multichannel data for understanding cognitive affordances during complex problem solving

This exploratory study challenges the current practices in cognitive load measurement by using multichannel data to investigate cognitive load affordances during online complex problem solving. Moreover, it is an attempt to investigate how cognitive load is related to strategy use. Accordingly, in the current study a well- and an ill-structured problem were developed in a virtual learning environment. Online support was provided. Participants were 15 students from the teacher training program. This study incorporated subjective measurements of students' cognitive load (i.e., intrinsic, extraneous, germane load and their mental effort) combined with physiological data containing galvanic skin response (GSR) and skin temperature (ST). A first aim was to investigate whether there was a significant difference for the subjective measurements, physiological data and consultation of support between the well-and ill-structured problem. Secondly this study investigated how individual differences of subjective measurements are related to individual differences of physiological data and consultation of support. Results reveal significant differences for intrinsic load, mental effort between a well- and ill-structured problem. Moreover, when investigating individual differences, findings reveal that GSR might be related to mental effort. Additionally, results indicate that cognitive load influences strategy use. Future research with larger sample sizes should verify these findings in order to have more insight into how we can measure cognitive load and how its related to self-directed learning. These insights should allow us to provide adaptive support in virtual learning environments.

[1]  Cristian Hofmann,et al.  Integrating cognitive load theory and concepts of human-computer interaction , 2010, Comput. Hum. Behav..

[2]  James B. Schreiber,et al.  Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review , 2006 .

[3]  Jodi Forlizzi,et al.  Psycho-physiological measures for assessing cognitive load , 2010, UbiComp.

[4]  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..

[5]  M. A. Rowe,et al.  Guide for Analysing Electrodermal Activity & Skin Conductance Responses for Psychological Experiments , 2013 .

[6]  Jeroen J. G. van Merriënboer,et al.  Perspectives on problem solving and instruction , 2013, Comput. Educ..

[7]  F. Paas.  Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. , 1992 .

[8]  T. Gog,et al.  Development of an instrument for measuring different types of cognitive load , 2013, Behavior Research Methods.

[9]  Martha E. Crosby,et al.  Assessing Cognitive Load with Physiological Sensors , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[10]  Ulrike Pfeil,et al.  Measuring Cognitive Load using Eye Tracking Technology in Visual Computing , 2016, BELIV '16.

[11]  R. Mayer,et al.  Nine Ways to Reduce Cognitive Load in Multimedia Learning , 2003 .

[12]  David H. Jonassen,et al.  Instructional design models for well-structured and III-structured problem-solving learning outcomes , 1997 .

[13]  Jeroen J. G. van Merriënboer,et al.  Training Complex Cognitive Skills: A Four-Component Instructional Design Model for Technical Training , 1997 .

[14]  Fred Paas,et al.  Effects of performance feedback valence on perceptions of invested mental effort , 2017 .

[15]  Fang Chen,et al.  Galvanic skin response (GSR) as an index of cognitive load , 2007, CHI Extended Abstracts.

[16]  T. Gog,et al.  Segmentation of Worked Examples: Effects on Cognitive Load and Learning , 2012 .

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

[18]  Katharina Scheiter,et al.  Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load? , 2009, Comput. Hum. Behav..

[19]  Gerhard Tröster,et al.  Discriminating Stress From Cognitive Load Using a Wearable EDA Device , 2010, IEEE Transactions on Information Technology in Biomedicine.

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

[21]  T. Schubert,et al.  When flanker meets the n-back: What EEG and pupil dilation data reveal about the interplay between the two central-executive working memory functions inhibition and updating. , 2015, Psychophysiology.

[22]  O Barnea,et al.  Spontaneous skin temperature oscillations in normal human subjects. , 1997, The American journal of physiology.

[23]  Tina Seufert,et al.  The interplay between self-regulation in learning and cognitive load , 2018, Educational Research Review.

[24]  T. Gog,et al.  Effects of concurrent monitoring on cognitive load and performance as a function of task complexity , 2011 .

[25]  Alexander Renkl,et al.  Learning from direct instruction: Best prepared by several self-regulated or guided invention activities? , 2017 .

[26]  Michalis Nik Xenos,et al.  Recognizing Emotions in Human Computer Interaction: Studying Stress Using Skin Conductance , 2015, INTERACT.

[27]  Yang Wang,et al.  Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks , 2012, OZCHI.

[28]  Yang Wang,et al.  Robust Multimodal Cognitive Load Measurement , 2016, Human–Computer Interaction Series.

[29]  Dominique Sluijsmans,et al.  Toward a Synthesis of Cognitive Load Theory, Four-Component Instructional Design, and Self-Directed Learning , 2009 .

[30]  Alistair A. Young,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2017, MICCAI 2017.

[31]  T. Gog,et al.  Measuring cognitive load with subjective rating scales during problem solving: differences between immediate and delayed ratings , 2015 .

[32]  Joris C Verster,et al.  The effect of stress on core and peripheral body temperature in humans , 2013, Stress.

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

[34]  Jan Elen,et al.  The influence of students' cognitive and motivational characteristics on students' use of a 4C/ID-based online learning environment and their learning gain , 2018, LAK.

[35]  J G Van MerriëNboerJeroen.  Perspectives on problem solving and instruction , 2013 .

[36]  J. Sweller.  Element Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load , 2010 .

[37]  Robert Z. Zheng,et al.  Solving complex problems: A convergent approach to cognitive load measurement , 2012, Br. J. Educ. Technol..

[38]  R. Nager,et al.  Skin temperature reveals the intensity of acute stress , 2015, Physiology & Behavior.

[39]  M. Boekaerts,et al.  Cognitive load and self-regulation: Attempts to build a bridge , 2017 .

[40]  F. Paas,et al.  Cognitive Load Measurement as a Means to Advance Cognitive Load Theory , 2003 .

[41]  Krista E. DeLeeuw,et al.  A Comparison of Three Measures of Cognitive Load: Evidence for Separable Measures of Intrinsic, Extraneous, and Germane Load , 2008 .