Literaturnachweis - Detailanzeige
| Autor/in | Rao, A. Ravishshankar |
|---|---|
| Titel | Interventions for Promoting Student Engagement and Predicting Performance in an Introductory Engineering Class |
| Quelle | In: Advances in Engineering Education, 8 (2020) 2, (23 Seiten)
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1941-1766 |
| Schlagwörter | Learner Engagement; Student Motivation; Prediction; Academic Achievement; Introductory Courses; Engineering Education; Educational Innovation; Textbooks; Electronic Learning; Films; Questioning Techniques; Discussion (Teaching Technique); Skill Development; Early Intervention; At Risk Students; College Students; Generational Differences; Class Size; Data Use; New Jersey |
| Abstract | Studies show that a significant fraction of students graduating from high schools in the U.S. is ill prepared for college and careers. Some problems include weak grounding in math and writing, lack of motivation, and insufficient conscientiousness. Academic institutions are under pressure to improve student retention and graduate rates, whereas students are under pressure to graduate and find employment. Consequently, there is room for substantial innovation in better motivating students, improving their performance, and helping them succeed. This paper describes two interventions consisting of cinematic meditation and online books. We utilized them to improve student engagement while developing important skills including conscientiousness and communication. In cinematic meditation, we engaged students in goal-directed and guided viewing of films concerning important technological developments. We administered this exercise during the first week of class, which helped to motivate students and stimulate classroom discussion. We asked students to write an essay describing their responses to questions related to the films. Online books help to log student activity and provide supplementary learning material for the students to master. Both these interventions produce quantitative data about student behavior, such as timeliness, which we used to predict subsequent student performance. A timeliness measure, obtainable as early as within 5 weeks of the semester, is correlated (r = 0.4, p < 0.001) with a cumulative score computed at the midpoint of the semester. This facilitates the early identification of at-risk students, and could avert further performance problems in the semester. The interventions and data-driven techniques described in this paper are easy to administer and scalable to large class sizes. (As Provided). |
| Anmerkungen | American Society for Engineering Education. 1818 N Street NW, Washington, DC 20036. Tel: 412-624-6815; Fax: 412-624-1108; Web site: http://advances.asee.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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