This emerging field of study combines the social sciences, computer science, and mathematics. According to the Society for Learning Analytics Research (SoLAR) founded by George Siemens, this field consists of measuring, collecting, analyzing, and reporting data collected in digital learning environments (e.g., ClassDojo, ChallengeU, Moodle) to glean information about students’ behaviours (e.g., how many times they click on a link, how much time they spend consulting a single resource) depending on their learning contexts. This data is analyzed to help teachers optimize their pedagogical actions (e.g., understanding, predicting, evaluating, providing feedback, and supporting learning) and improve their learning environments and tools for one or more students.
Resources to innovate
In this podcast, the speakers discuss the ethical challenges that can arise when analyzing learning data. These challenges include the nature and true relevance of the data to be collected, the risk of labelling students (evaluation bias), the importance of not jumping to conclusions based on one or more pieces of data without paying attention to the quality of the learning scenario, the level of difficulty of the activities at hand, and monitoring how the data collected is processed and distributed to other players.
In this article, the authors share the results of a case study conducted in a university course about learning analytics techniques and data, involving four online assessments (reading comprehension questions, a discussion forum, reflection questions, project-based learning), which proved most conducive for providing support in an online teaching and learning framework (e.g., frequency of peer interactions, qualitative and quantitative text analysis, viewing artifacts).
In this article, the authors present the results of a study conducted to demonstrate the extent to which learning analytics data can help teachers improve students’ learning process (e.g., support their progress) before asking them to conduct online formative assessment activities (e.g., integrating immediate interactive feedback into the activities).
This resource defines learning analytics and highlights its benefits. It also discusses the challenges and risks that might be associated with this field of study and offers a series of recommendations. Finally, it provides teachers with an explanation of how learning analytics can help us think about assessments in new ways, which can only lead to improvements.
This resource explains learning analytics to teachers. It outlines many reasons why this field is relevant and highlights three concrete benefits: generating reports to track student progress, interpreting data to find answers to questions (e.g., Is my course appealing to my students?), and coming up with remedial hypotheses (e.g., making a course more engaging through storytelling).
This resource is the first edition of a handbook on the subject of learning analytics. It is primarily intended for teachers who want to better understand the ins and outs of this field of study and how they can put it into practice to benefit students and their educational institutions. Four topics are discussed: 1) The fundamental concepts of learning analytics from both a historical and theoretical perspective; 2) Techniques and approaches based on case studies; 3) Applications in light of models and methods; 4) Institutional strategies and future prospects.
In this pedagogical guide, the authors use the lens of learning analytics to propose a new version of the Multiple Measures model developed by researcher Victoria Bernhardt in 1998. In this new version, they highlight how demographics, learning, and school processes influence one another. Similarly, they invite people who work in education to consider the influence of these data in order to make decisions that consider the complexity of the educational system as a whole (e.g., the impact of support programs on learning, the impact of learning programs on support programs).
In this blog post, the author points out how useful learning analytics data can be for directors of teaching programs. For example, once analyzed, these data can help us improve our understanding of learning contexts and support changes in teaching practices. They can also contribute to better decisions and the implementation of institutional strategies to develop programs of study that can make a difference when it comes to supporting students’ learning (e.g., acquiring new educational resources with diligence, taking responsibility).
In this article, the author explains what learning analytics consists of, using ever-increasing data (in terms of volume, type), namely as a result of the many learning activities that students are asked to perform in digital learning environments. It explains how learning analytics can help practitioners and researchers in postsecondary education environments adjust their educational decisions and actions (e.g., to better motivate students and target those who are at risk of dropping out), thereby improving the teaching/learning experience.
In this blog post, the authors reflect on the 7th edition of the International Conference on Learning Analytics and Knowledge, which was held in Vancouver in March 2017 and attended by around 1,000 practitioners and researchers in the field of education. The post expounds upon initiatives and research findings surrounding the topic of learning analytics and shares some of the challenges posed by the field, such as ethical issues and the protection of students’ personal data.
This article presents Fovéa, a dashboard-style learning analytics tool aimed at teachers who want to obtain real-time data on students’ perceptions, motivation, and stress levels in an effort to provide them with a personalized learning experience using intervention strategies that support academic success.