Abstract

Effective use of learning analytics systems has been purported to confer various benefits to learners in terms of both attainment and retention. There is, however, little agreement on which data are meaningful or useful. Whilst measures of engagement might correlate with outcomes, thereby retrospectively ‘predicting’ them, there are fewer studies which attempt to predict using ‘live’ system data in a face-to-face teaching environment. This study reports an analysis of week by week data from a learning analytics system which monitored 1,602 first year UK undergraduates. Uniquely, although students could view their own data, no formal interventions took place. Results showed that students who obtained the highest end-of-year marks were more likely to be in a higher engagement quintile as early as the first 3–4 weeks, and that early engagement was highly predictive of future engagement. Students who started in a higher engagement quintile, but their engagement decreased, were more likely to have higher marks than those that started in a lower quintile and then increased their engagement. Early measures of engagement are therefore predictive of future behaviour and of future outcomes, a finding which has important implications for universities wishing to improve student outcomes.

摘要

据称，有效使用学习分析系统可以为学习者在成绩和保留方面带来各种好处。然而，对于哪些数据有意义或有用，几乎没有一致意见。虽然参与度的衡量可能与结果相关，从而可以回顾性地“预测”它们，但很少有研究试图在面对面的教学环境中使用“实时”系统数据进行预测。这项研究报告了对来自学习分析系统的每周数据的分析，该系统监控了 1,602 名英国一年级本科生。独特的是，尽管学生可以查看自己的数据，但并未进行正式干预。结果表明，获得最高年终分数的学生更有可能早在前 3-4 周就处于较高的参与度五分之一，并且早期参与对未来参与具有高度预测性。从参与度较高的五分之一开始的学生，但他们的参与度下降，与那些以较低的五分之一开始然后提高参与度的学生相比，他们更有可能获得更高的分数。因此，早期参与度衡量可以预测未来的行为和未来的结果，这一发现对希望提高学生成绩的大学具有重要意义。