评估本科计算课程中的教学激励：一种使用学习分析的混合方法
Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics
AI总结：
本文采用学习分析与定性数据的混合方法，评估了University College London本科计算模块中新教学激励措施对学生参与度的影响，并提出了一个整合数据驱动分析与学生视角的可解释参与度模型，以帮助教育工作者改进教学策略。
摘要：
在COVID-19后高等教育动态演变的背景下，本文评估了在University College London的一年级本科计算模块中实施的新教学激励措施的影响。我们采用混合方法，将学习分析与定性数据相结合，以评估这些激励措施在提高学生参与度方面的有效性。通过对204名学生的Moodle活动日志进行Bayesian network分析，绘制了资源交互的纵向概览。该分析将早期资源参与确定为持续参与的预测指标，同时表明新激励措施不成比例地使高参与度学生受益。焦点小组讨论补充了这一分析，提供了学生对教学变革和模块设计看法的见解。这些定性发现强调了通过新激励措施维持参与度的挑战，并突出了混合学习环境中沟通的重要性。我们的论文引入了一个可解释且可操作的学生参与度模型，该模型整合了客观的、数据驱动的分析与学生的视角。该模型为教育工作者提供了评估和改进教学策略的工具。通过展示我们的混合方法在捕捉数字学习环境中学生行为复杂性方面的有效性，我们强调了该模型在改善不同教育环境中在线教学实践的潜力。
英文摘要：
In the context of higher education's evolving dynamics post-COVID-19, this paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London. We employ a mixed methods approach, combining learning analytics with qualitative data, to evaluate the effectiveness of these incentives on increasing student engagement. A longitudinal overview of resource interactions is mapped through Bayesian network analysis of Moodle activity logs from 204 students. This analysis identifies early resource engagement as a predictive indicator of continued engagement while also suggesting that the new incentives disproportionately benefit highly engaged students. Focus group discussions complement this analysis, providing insights into student perceptions of the pedagogical changes and the module design. These qualitative findings underscore the challenge of sustaining engagement through the new incentives and highlight the importance of communication in blended learning environments. Our paper introduces an interpretable and actionable model for student engagement, which integrates objective, data-driven analysis with students' perspectives. This model provides educators with a tool to evaluate and improve instructional strategies. By demonstrating the effectiveness of our mixed methods approach in capturing the intricacies of student behaviour in digital learning environments, we underscore the model's potential to improve online pedagogical practices across diverse educational settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
