The impact of frequency and stakes of formative assessment on student achievement in higher education: A learning analytics study.
Background: Research shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, lead...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 1; pp. 1 - 12 |
|---|---|
| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2025
|
| 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=183981436&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183981436 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2025 vid: 41 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183981436 180819079 183981436 183981436 10.1111/jcal.13087 183981436 ppf: 1 ppct: 11 formats: tig: atl: The impact of frequency and stakes of formative assessment on student achievement in higher education: A learning analytics study. aug: au: Bulut, Okan Gorgun, Guher Yildirim‐Erbasli, Seyma Nur affil: Centre for Research in Applied Measurement and Evaluation, Faculty of Education, University of Alberta, Edmonton, Canada sug: subj: Education, Baccalaureate Students, Undergraduate Academic Achievement Educational Measurement Learning Human Canada Colleges and Universities Analytic Research Student Performance Appraisal Education, Competency-Based Educational Status Multiple Regression Models, Theoretical Computer-Assisted Instruction Data Analysis Software Descriptive Statistics ab: Background: Research shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, leading to better tracking of learning progress. Optional assessments may encourage voluntary engagement, potentially leading to a more genuine reflection of student understanding. Also, frequent assessments provide continuous opportunities for feedback and adjustment, which can keep students actively engaged in the learning process. Objectives: This study aims to investigate two crucial facets of formative assessments: frequency and the level of stakes involved (mandatory vs. optional). We examine how modifying the frequency of formative assessments affects students' course performance. Additionally, we evaluate the impact of mandatory versus optional formative assessments on students' course performance in higher education. Methods: The sample of this study consisted of undergraduate students (n = 336) enrolled in three sections of a large asynchronous course at a Canadian university. We extracted features associated with online formative assessments (e.g., the number of attempts and average scores) from the learning management system. Next, we used these features to predict students' performance in summative assessments (two midterms and a final exam). Results and Conclusions: Our findings indicated that increasing the frequency of online formative assessments did not consistently improve student performance. Also, participation frequency in online formative assessments seemed to vary depending on assessment stakes (i.e., optional vs. mandatory). We recommend that instructors examine what conditions can maximize the contribution of formative assessments to students' academic achievement before building predictive models. Lay Description: What is already known about this topic: Higher education institutions often use learning analytics for the early identification of low‐performing students or students at risk of dropping out.Research suggests that online formative assessments can yield important indicators of learning that can be used in predictive learning analytics models.However, students may not be motivated to participate in online formative assessments since these assessments are often optional and ungraded. Thus, increasing the number of formative assessments may not result in better learning outcomes. What this paper adds: We investigated whether the frequency and stakes (i.e., optional vs. mandatory) of online formative assessments could influence their utility in predicting students' performance.Three sections of a large asynchronous course used different strategies in incorporating online formative assessments into their instruction by modifying the number of online formative assessments administered before each summative assessment. Also, participation in online formative assessments was mandatory in one section, while it was entirely optional in the other two sections.We extracted a set of features from online formative assessments (e.g., the number of attempts in each formative assessment and average scores) to predict students' performance in summative assessments throughout the semester.Online formative assessments were a strong predictor of student performance earlier in the semester, but their predictive power decreased gradually throughout the semester.Offering formative assessments more frequently appeared to improve the predictive utility of scores and other indicators (e.g., number of attempts) extracted from formative assessments.Optional formative assessments seem to yield more predictive indicators of student performance. Implications for practice and/or policy: This study provides evidence for the utility of online formative assessments as predictors of student performance.Our findings suggest that using online formative assessments more strategically (i.e., in fewer quantities and as mandatory assessments for students) may enhance their predictive power.Online formative assessments appear to yield more predictive features earlier in the semester, making them more valuable for early identification of at‐risk and low‐performing students. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|