CSCLRec: Personalized Recommendation of Forum Posts to Support Socio-Collaborative Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).

Autor/inn/en	Chen, Zhaorui; Demmans, Carrie
Titel	CSCLRec: Personalized Recommendation of Forum Posts to Support Socio-Collaborative Learning [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).
Quelle	(2020), (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Cooperative Learning; Computer Mediated Communication; Recordkeeping; Online Courses; Graphs; Data Analysis; Socialization; Social Networks; Network Analysis; Natural Language Processing; Student Behavior; Interaction; Prediction; Preferences; Learning Activities; Information Systems; Graduate Students; Profiles; Foreign Countries; Canada (Toronto) + Suchen Sie Ihr Suchwort? Kooperatives Lernen; Computerkonferenz; Leistungsnachweis; Online course; Online-Kurs; Grafische Darstellung; Auswertung; Socialisation; Sozialisation; Social network; Soziales Netzwerk; Netzplantechnik; Natürliche Sprache; Student behaviour; Schülerverhalten; Interaktion; Vorhersage; Lernaktivität; Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin; Charakterisierung; Profilanalyse; Ausland
Abstract	Discussion forums are used to support socio-collaborative learning processes among students in online courses. However, complex forum structures and lengthy discourse require that students spend their limited time searching and filtering through posts to find those that are relevant to them rather than spending that time engaged in other meaningful learning activities (i.e., discussion). Moreover, existing adaptive systems do not accommodate individual learner needs in these contexts. In this work, we propose a multi-relational graph-based recommendation approach that mines student interaction logs to address the above problems within discussionbased socio-collaborative online courses. To account for the social aspects of learning, our approach incorporates learner modeling, social network analysis, and natural language processing techniques; it offers tailored recommendations of forum posts for learners with different types of interaction behaviors. In our experiments with small online courses, our approach outperformed competitor approaches in terms of recommendation precision while meeting expectations with respect to diversity and novelty. The results illustrate the proposed algorithm's effectiveness in predicting student preferences, suggesting its potential to increase student participation in discussion-related learning activities. [For the full proceedings, see ED607784.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01