Short answers to deep questions: supporting teachers in large-class settings.
In large class settings, individualized student-teacher interaction is difficult. However, teaching interactions (e.g., formative feedback) are central to encouraging deep approaches to learning. While there has been progress in automatic short-answer grading, analysing student responses to support...
| Published in: | Journal of Computer Assisted Learning Vol. 33; no. 4; pp. 306 - 320 |
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| Main Authors: | , , , |
| Format: | research tables/charts Journal Article |
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Wiley-Blackwell
Aug2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123928214&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123928214 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2017 vid: 33 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 123928214 123928214 123928214 10.1111/jcal.12178 123928214 ppf: 306 ppct: 14 formats: tig: atl: Short answers to deep questions: supporting teachers in large-class settings. aug: au: McDonald, J. Bird, R.J. Zouaq, A. Moskal, A.C.M. affil: Centre for Learning and Research in Higher Education, University of Auckland, New Zealand sug: subj: Teaching Methods Feedback Faculty-Student Relations Education, Medical New Zealand Human Natural Language Processing T-Tests P-Value New Zealand Blood Circulation Funding Source ab: In large class settings, individualized student-teacher interaction is difficult. However, teaching interactions (e.g., formative feedback) are central to encouraging deep approaches to learning. While there has been progress in automatic short-answer grading, analysing student responses to support formative feedback at scale is arguably some way from being widely applied in practice. However, analysing student written responses can provide insights into student conceptions, thus directly informing teacher actions. Indeed, we argue that analysing student responses to provide feedback directly to teachers is as worthy a goal as providing individualized feedback to students and is achievable given the current state-of-the-art in natural language processing. In this paper, we analyse student written responses to short-answer questions posed in the context of a large first year health sciences course. Each question was designed to elicit deep responses. Our qualitative analysis illustrates the variability in student responses and reveals multiple relationships between these responses, course materials and the questions posed. Such information can be invaluable for teacher praxis. We conclude with a conceptual 'dashboard' that categorizes student responses and reveals relationships between responses, course resources and the questions. Such a dashboard could provide timely, actionable insights for teachers and help foster deep learning approaches for students. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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