Analytics of time management strategies in a flipped classroom.
This paper aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. Specifically, an exploratory study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N = 1,134)....
| Publicado en: | Journal of Computer Assisted Learning Vol. 36; no. 1; pp. 70 - 89 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141167015&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141167015 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2020 vid: 36 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 141167015 141167015 141167015 10.1111/jcal.12392 141167015 ppf: 70 ppct: 19 formats: tig: atl: Analytics of time management strategies in a flipped classroom. aug: au: Ahmad Uzir, Nora'ayu Gašević, Dragan Matcha, Wannisa Jovanović, Jelena Pardo, Abelardo affil: School of Informatics, The University of Edinburgh, Edinburgh UK sug: subj: Data Analytics Time Management Learning Methods Learning Environment Student Attitudes Human Exploratory Research Education, Graduate Algorithms Students, Undergraduate Software Strategic Planning Feedback Academic Performance ab: This paper aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. Specifically, an exploratory study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N = 1,134). Trace data about activities were initially coded for the timeliness of activity completion. Such data were then analysed using agglomerative hierarchical clustering based on Ward's algorithm, first order Markov chains, and inferential statistics to (a) detect time management tactics and strategies from students' learning activities and (b) analyse the effects of personalized analytics‐based feedback on time management. The results indicate that meaningful and theoretically relevant time management patterns can be detected from trace data as manifestations of students' tactics and strategies. The study also showed that time management tactics had significant associations with academic performance and were associated with different interventions in personalized analytics‐based feedback. Lay Description: What is currently known about the subject matter: Effective time management behaviour is a vital element for self‐regulation as well as a strong predictor of academic success.The state of the art in time management research is mainly based on self‐reports.External feedback in educational research is one of the most powerful intervention strategies to optimize student learning progress and performance.The advantage of analytics approaches is in their reliance on trace data that can be captured without significant interference with the actual learning process and with low risk of bias that traditional data collection methods are often susceptible to. What our paper adds to this: We detected time management tactics and strategies adopted by students in online component of a flipped classroom through a combined use of trace data and analytics techniques.We identified the association between time management strategy groups (made up from a set of enacted tactics) identified in an online component of a flipped classroom course and their course performance.We analysed the effects of personalized analytics‐based feedback on time management. The implications of study findings for practitioners: From a research perspective, this study proposed methodology that could help both researchers and practitioners improve the interpretation of their results related to time management behaviour.From an instructor perspective, this study makes a step forward to translate time management into actionable feedback to promote better learning engagement.The study also suggests that for instructors to promote effective time management strategies, they can provide personalized analytics‐based feedback to the students in the first half of a semester on a weekly basis rather than providing the feedback throughout the entire duration of a course.From a learner perspective, this study could offer practical guidelines for making necessary adaption and adjustment of their timing of engagement based on a different set of time management tactics, to effectively regulate their learning time, especially during the preparation for face‐to‐face sessions. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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