A Dataset of Learnersourced Explanations from an Online Peer Instruction Environment [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).

Autor/inn/en	Bhatanagar, Sameer; Zouaq, Amal; Desmarais, Michel C.; Charles, Elizabeth
Titel	A Dataset of Learnersourced Explanations from an Online Peer Instruction Environment [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).
Quelle	(2020), (6 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Peer Teaching; Electronic Learning; Educational Technology; Web Based Instruction; Homework; Logical Thinking; Data Collection; Persuasive Discourse; STEM Education; Peer Evaluation; Biology; Chemistry; Physics; College Science; College Students + Suchen Sie Ihr Suchwort? Peer group teaching; Peer Group Teaching; Unterrichtsmedien; Web Based Training; Hausaufgabe; Data capture; Datensammlung; Persuasion; Persuasive Kommunikation; STEM; Biologie; Chemie; Physik; Collegestudent
Abstract	Online "Peer Instruction" has become prevalent in many "flipped classroom" settings, yet little work has been done to examine the content students generate in such a learning environment. This study characterizes a dataset generated by an open-source, web-based homework system that prompts students to first answer questions, and then provide explanations of their reasoning. Of particular interest in this dataset, is that students are also prompted to evaluate a subset of peer explanations based on how convincing they are, as part of the Peer Instruction learning script. Since these student "votes" are then used in the selection of what is shown to future learners, we cast this as an instance of "learnersourcing," a paradigm that presents new research opportunities for the Learning Analytics community. This study characterizes a dataset from one "Peer Instruction" tool, that includes not only the student generated answers and explanations, but this novel "vote" attribute, which aims to capture how convincing each explanation is to other learners. The dataset includes longitudinal observations of student responses over the course of a semester, following groups from three STEM disciplines. The data is made available to interested researchers. [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