利用 iLearn Insights 进行学习分析

Shamim Joarder
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In contrast to systems that provide only static data reports, this vendor-independent innovation was built in-house to analyse and visualise student learning data in relation to access patterns, forum activity, learning activity submission and grades to identify a student cohort that needs support, as well as enabling teaching staff to send personalised emails to students on the basis of their level of engagement and/or performance. Feedback has a powerful impact on learning, but students frequently highlight it as an area that can be improved in tertiary education (Dawson and Henderson, 2019). iLearn Insights provides graphical representation of LMS data that enables visual personalised feedback to students. Within three mouse clicks, academic staff can trigger a range of automated communications to commend high-achieving students, offer additional assistance to lower performing students or to recapture disengaged students. These emails can be a targeted to a group of students and configured by the unit convenor to contain motivating information including the top five resources accessed by classmates; a student’s mark in comparison to the class average; number or percentage of students that have already submitted an assignment; clickable links; and support resources for students falling behind. ILearn Insights was developed based on four principals of learning analytics design knowledge: integration, agency, reference frame and dialogue (Wise, 2014). It has been observed that targeted visual feedback with clickable links is the most effective way to engage students quickly.?The positive impact of iLearn Insights is demonstrated by its rapid uptake by teaching staff across Macquarie University. When it launched in Session 1, 2020, after 18 months of piloting, there were 478 users across 763 units (subjects) who sent 125334 targeted personalised emails. 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摘要

iLearn Insights 是一款创新型在线应用软件,用于解决学生参与、激励和支持方面的难题,为面临失败风险的学生自动发送个性化电子邮件。它能让学术界识别出哪些学生在学习管理系统(LMS)内容方面存在问题,从而进行早期干预,并通过为学生群体提供有针对性的支持,最终提高学生的保留率。与只提供静态数据报告的系统不同,这种独立于供应商的创新是在内部建立的,可以分析和可视化与访问模式、论坛活动、学习活动提交和成绩有关的学生学习数据,以确定需要支持的学生群体,并使教学人员能够根据学生的参与程度和/或表现向他们发送个性化电子邮件。 反馈对学习具有强大的影响,但学生经常强调这是高等教育中有待改进的一个方面(Dawson 和 Henderson,2019 年)。只需点击三下鼠标,教职员工就能触发一系列自动通信,表扬成绩优秀的学生,为成绩较差的学生提供额外帮助,或重新吸引脱离学习的学生。这些电子邮件可以针对一组学生,并由单元召集人进行配置,以包含激励信息,包括同学访问的前五大资源、学生分数与班级平均分的比较、已提交作业的学生人数或百分比、可点击链接以及为落后学生提供的支持资源。 ILearn Insights的开发基于学习分析设计知识的四个原则:整合、代理、参照系和对话(Wise,2014)。据观察,带有可点击链接的有针对性的视觉反馈是快速吸引学生的最有效方式。经过 18 个月的试点,iLearn Insights 在 2020 年第一学期推出时,共有 478 名用户,涉及 763 个单元(科目),发送了 125334 封有针对性的个性化电子邮件。在 2023 年第一季度,40 个系和学习支持领域以及 1237 个单位的约 808 名用户使用了 iLearn Insights,发送了超过 316576 封有针对性的电子邮件,以鼓励学生参与学习活动或提供支持。这意味着三年内用户数量增加了 169%,单位数增加了 162%,电子邮件数量增加了 253%。这些个性化的电子邮件交流提高了学生的参与度,这对学生的成功至关重要(Kahu 和 Nelson,2018 年;De Villiers 和 Werner,2018 年;McClenney 等人,2012 年;Klem 和 Connell,2004 年)。
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Learning analytics with iLearn Insights
iLearn Insights is an innovative online application to address the challenge of engaging, motivating and supporting students with automated personalised email for those at risk of failure. It allows academics to identify students who are engaging or otherwise with the Learning Management System (LMS) content, thus allowing early intervention, and ultimately improved retention in units by providing targeted support to a student cohort. In contrast to systems that provide only static data reports, this vendor-independent innovation was built in-house to analyse and visualise student learning data in relation to access patterns, forum activity, learning activity submission and grades to identify a student cohort that needs support, as well as enabling teaching staff to send personalised emails to students on the basis of their level of engagement and/or performance. Feedback has a powerful impact on learning, but students frequently highlight it as an area that can be improved in tertiary education (Dawson and Henderson, 2019). iLearn Insights provides graphical representation of LMS data that enables visual personalised feedback to students. Within three mouse clicks, academic staff can trigger a range of automated communications to commend high-achieving students, offer additional assistance to lower performing students or to recapture disengaged students. These emails can be a targeted to a group of students and configured by the unit convenor to contain motivating information including the top five resources accessed by classmates; a student’s mark in comparison to the class average; number or percentage of students that have already submitted an assignment; clickable links; and support resources for students falling behind. ILearn Insights was developed based on four principals of learning analytics design knowledge: integration, agency, reference frame and dialogue (Wise, 2014). It has been observed that targeted visual feedback with clickable links is the most effective way to engage students quickly.?The positive impact of iLearn Insights is demonstrated by its rapid uptake by teaching staff across Macquarie University. When it launched in Session 1, 2020, after 18 months of piloting, there were 478 users across 763 units (subjects) who sent 125334 targeted personalised emails. In Session 1, 2023 iLearn Insights was used by approximately 808 users across 40 departments and learning support areas and 1237 units, sending over 316576 targeted emails to encourage students to engage with learning activities or offer support. That represents an increase of 169% of users, 162% of units and 253% of emails in three years. These personalised email exchanges have led to enhanced student engagement, which is critical for student success (Kahu and Nelson, 2018; De Villiers and Werner, 2018; McClenney et al, 2012; Klem and Connell, 2004).
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