Utilisation of Learning Analytics to Identify Students at Risk of Poor Academic Performance in Medical Schools

被引:0
|
作者
Wong, Thai Ling [1 ]
Hope, David [1 ]
Jaap, Alan [1 ]
机构
[1] Univ Edinburgh, Med Educ, Edinburgh, Scotland
关键词
academic performance/grades; preclinical education; online medical education; learner engagement; learning analytics; ATTENDANCE;
D O I
10.7759/cureus.66278
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Introduction Identifying students at risk of failure before they experience difficulties may considerably improve their outcomes. However, identification techniques can be costly, time-intensive, and of unknown efficacy. Medical educators need accessible and cost-effective ways of identifying at-risk students. The aim of this study was to investigate the relationship between student engagement in an online classroom and academic performance given the transition of many courses from in-person to online learning. Methods A retrospective study was conducted on a group of 235 students from the University of Edinburgh Bachelor of Medicine and Surgery (MBChB) in Year One for eight weeks from the start of term, September 2020. Purposive sampling was used. Data were collected on total test submissions, total discussion board submissions, engagement scores, and overall exam scores. Learning analytics on discussion board engagement were collected for new medical students before they had sat any summative assessment. Tests completed, discussion board posts made, and their total engagement score were correlated with their first summative assessment scores at the end of semester one. Results We found a statistically significant correlation between total test submissions, total discussion board submissions, engagement scores, and overall exam scores, with small-medium effects (r = 0.281, p<0.001) (r = 0.241, p<0.001), and (r = 0.202, p<0.001). Students with more test submissions, total discussion board submissions, and total engagement had a higher overall exam score. There was a statistically significant moderate correlation between total submissions and overall exam scores (r = 0.324, p<0.001). Conclusions Students who had a higher number of submissions were more likely to perform better on assessments. Early engagement correlates with performance. Learning analytics can help identify student underperformance before they undertake any assessment, and this can be done very inexpensively and with minimal staff resources if properly planned.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] Identifying and characterizing students suspected of academic dishonesty in SPOCs for credit through learning analytics
    Jaramillo-Morillo, Daniel
    Ruiperez-Valiente, Jose
    Sarasty, Mario F.
    Ramirez-Gonzalez, Gustavo
    INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION, 2020, 17 (01)
  • [22] Identifying and characterizing students suspected of academic dishonesty in SPOCs for credit through learning analytics
    Daniel Jaramillo-Morillo
    José Ruipérez-Valiente
    Mario F. Sarasty
    Gustavo Ramírez-Gonzalez
    International Journal of Educational Technology in Higher Education, 17
  • [23] Evaluation of Academic Performance Based on Learning Analytics and Ontology: a Systematic Mapping Study
    Costa, Laecio A.
    Salvador, Lais N.
    Amorim, Ricardo R.
    2018 IEEE FRONTIERS IN EDUCATION CONFERENCE (FIE), 2018,
  • [24] The effectiveness of learning analytics for identifying at-risk students in higher education
    Foster, Ed
    Siddle, Rebecca
    ASSESSMENT & EVALUATION IN HIGHER EDUCATION, 2020, 45 (06) : 842 - 854
  • [25] Monitoring Students Performance in E-learning based on Learning Analytics and Learning Educational Objectives
    Costa, Laecio Araujo
    das Santos e Souza, Mario Vieira
    Salvador, Lais do Nascimento
    Rocha Amorim, Ricardo Jose
    2019 IEEE 19TH INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT 2019), 2019, : 192 - 193
  • [26] Learning Analytics and Evaluative Mentoring to increase the students' performance in Computer Science
    Ruiz-Ferrandez, M.
    Ortega, G.
    Roca-Piera, J.
    PROCEEDINGS OF 2018 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON) - EMERGING TRENDS AND CHALLENGES OF ENGINEERING EDUCATION, 2018, : 1297 - 1304
  • [27] Variables that influence the performance of graduate students: A perspective from the learning analytics
    Urbina-Najera, Argelia B.
    TELOS-REVISTA DE ESTUDIOS INTERDISCIPLINARIOS EN CIENCIAS SOCIALES, 2021, 23 (01): : 36 - 50
  • [28] Explainable learning analytics to identify disengaged students early in semester: an intervention supporting widening participation
    Linden, Kelly
    van der Ploeg, Neil
    Roman, Noelia
    JOURNAL OF HIGHER EDUCATION POLICY AND MANAGEMENT, 2023, 45 (06) : 626 - 640
  • [29] An Overview Of Studies About Students' Performance Analysis and Learning Analytics in MOOCs
    Duru, Ismail
    Dogan, Gulustan
    Diri, Banu
    2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2016, : 1719 - 1723
  • [30] The effect of learning analytics assisted recommendations and guidance feedback on students' metacognitive awareness and academic achievements
    Yilmaz, Fatma Gizem Karaoglan
    JOURNAL OF COMPUTING IN HIGHER EDUCATION, 2022, 34 (02) : 396 - 415