Exploring Students' Cognitive and Affective States during Problem Solving through Multimodal Data: Lessons Learned from a Programming Activity

Autor/inn/en	Mangaroska, Katerina; Sharma, Kshitij; Gaševic, Dragan; Giannakos, Michail
Titel	Exploring Students' Cognitive and Affective States during Problem Solving through Multimodal Data: Lessons Learned from a Programming Activity
Quelle	In: Journal of Computer Assisted Learning, 38 (2022) 1, S.40-59 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Mangaroska, Katerina) ORCID (Sharma, Kshitij)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0266-4909
DOI	10.1111/jcal.12590
Schlagwörter	Cognitive Processes; Psychological Patterns; Problem Solving; Programming; Coding; Metacognition; Prior Learning; Eye Movements; Computer Science Education; College Students; Troubleshooting; Difficulty Level; Instructional Design; Learning Analytics + Suchen Sie Ihr Suchwort? Cognitive process; Kognitiver Prozess; Problemlösen; Programmierung; Codierung; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Vorkenntnisse; Augenbewegung; Computer science lessons; Informatikunterricht; Collegestudent; Störfall; Schwierigkeitsgrad; Lesson concept; Lessonplan; Unterrichtsentwurf
Abstract	Background: Problem-solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE-log data). Therefore, how cognition and affect intertwine and unfold over time in programming problem-solving activities are rarely investigated. Objectives: In this study, we examined how the theory-informed measures from multimodal data that we have selected as proxies for cognitive and affective dimensions of learning, are associated with student performance, and in comparison, to prior-knowledge. Methods: A high-frequency temporal data was collected with a camera, an electroencephalogram, and an eye-tracker from 40 computer science students (bachelor and master studies) in the context of a code-debugging activity. To study the cognitive processes associated with learning we focused on cognitive load theory (CLT) and the human information processing model. In addition, we complemented CLT with the model of affective dynamics in learning to avoid the machine reductionism perspective. Results: Our findings demonstrated that attention, convergent thinking, and frustration were positively correlated with students' successful code-debugging (i.e., performance), and frequently manifested by high performing participants. Cognitive load, memory load, and boredom were negatively correlated with students' performance, and typically manifested by low performing participants. Implications: Extending the context of analysis in reference to student cognitive processes and affective states, affords educators not just to identify lower performers, but also to understand the potential reasons behind their performance, making our method an important contribution in the confluence of CER and the learning technology communities. In addition, the insights extracted from our analyses allow us to discuss potential avenues for improving learning design and the design of interactive learning systems to support the development of students' problem-solving skills. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01