Self-Assessment Task Processing Behavior of Students in Higher Education [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).

Autor/inn/en	Kasakowskij, Regina; Haake, Joerg M.; Seidel, Niels
Titel	Self-Assessment Task Processing Behavior of Students in Higher Education [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).
Quelle	(2023), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Self Evaluation (Individuals); Student Behavior; College Students; Learning Processes; Computer Science Education; Feedback (Response); Behavior Patterns; Educational Technology; Technology Uses in Education + Suchen Sie Ihr Suchwort? Student behaviour; Schülerverhalten; Collegestudent; Learning process; Lernprozess; Computer science lessons; Informatikunterricht; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen
Abstract	Improving competence requires practicing, e.g. by solving tasks. The Self-Assessment task type is a new form of scalable online task providing immediate feedback, sample solution and iterative improvement within the newly developed SAFRAN plugin. Effective learning not only requires suitable tasks but also their meaningful usage within the student's learning process. So far, learning processes of students working on such Self-Assessment tasks have not been studied. Thus, SAFRAN was extended with activity logging allowing process mining. SAFRAN was used in a first-year computer science university course. Students' behavior was clustered and analyzed using log data. 3 task completion behavior patterns were identified indicating positive, neutral or negative impact on task processing. Differences in the use of feedback and sample solutions were also identified. The results are particularly relevant for instructors who can tailor adaptive feedback content better to its target group. The analytics approach described may be useful for researchers who want to implement and study adaptive and personalized task processing support. [For the complete proceedings, see ED630829.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01