Early warning systems for more effective student counselling in higher education: Evidence from a Dutch field experiment

Abstract

Early Warning Systems (EWS) in higher education accommodate student counsellors by identifying at-risk students and allow them to intervene in a timely manner to prevent student dropout. This study evaluates an EWS that shares student-specific risk information with student counsellors, which was implemented at a large Dutch university. A randomised field experiment was conducted to estimate the effect of EWS-assisted counselling on first-year student dropout and academic performance. The results show that the EWS accurately predicts at-risk students. Yet, EWSassisted counselling did not reduce dropout, nor improved academic performance. Solving the underlying problem of poor academic performance might require additional actionable feedback and recommended counselling practices. SamenvattingEarly Warning Systems (EWS) ondersteunen studieadviseurs in het hoger onderwijs door studenten in nood te signaleren en zo vroegtijdige interventies mogelijk te maken om studentuitval te voorkomen. Deze studie evalueert de implementatie van een EWS die student-specifieke risico-informatie deelde met studieadviseurs op een grote Nederlandse universiteit. Om het effect van EWS-ondersteund advies op

Bringing Transparency to Predictive Analytics: A Systematic Comparison of Predictive Modeling Methods in Higher Education

“…A final question is whether predictive analytics are actually resulting in more effective targeting of and support for at-risk students in higher education. While few studies to date have examined the effects of predictive analytics on college academic performance, persistence, and degree attainment, the three experimental studies of which we are aware find limited evidence of positive effects for at-risk students (Alamuddin et al, 2019;Milliron et al, 2014;Plak et al, 2019). More research is needed to understand the role of predictive analytics in improving institutional performance.…”

“…Colleges and universities use predictive analytics for various purposes, ranging from identifying students who might default on their loans to targeting alumni who are likely to give generously to the institution (Ekowo & Palmer, 2016). The most common use of predictive analytics, however, is to identify students at risk of failing courses or dropping out (Alamuddin et al, 2019; Milliron et al, 2014; Plak et al, 2019), and to direct various student success strategies (e.g., intrusive advising, additional financial aid) to these students. Numerous contextual factors have motivated institutions to turn toward predictive analytics.…”

Insights of Instructors and Advisors into an Early Prediction Model for Non-Thriving Students

“…Therefore, a short meeting may serve as the first low-cost step towards assisting the detected student. However, this may not be sufficient in preventing the predicted outcomes (Plak et al, 2022).…”

Designing and Evaluating a Big Data Analytics Approach for predicting students’ success factors

“…The providers can extend academic support to students through quality learning and teaching to enhance their academic performance. Early and timely identi cation of students at risk by using any Information System (IS) can support the HE providers to take appropriate measures effectively to enhance student academic progress (13)(14)(15). For example, an Educational Decision Support System (DSS) can be considered a paramount IS to support the appropriate relevant decision (16).…”