OpenEssayist: a supply and demand learning analytics tool for drafting academic essays

This paper focuses on the use of a natural language analytics engine to provide feedback to students when preparing an essay for summative assessment. OpenEssayist is a real-time learning analytics tool, which operates through the combination of a linguistic analysis engine that processes the text in the essay, and a web application that uses the output of the linguistic analysis engine to generate the feedback. We outline the system itself and present analysis of observed patterns of activity as a cohort of students engaged with the system for their module assignments. We report a significant positive correlation between the number of drafts submitted to the system and the grades awarded for the first assignment. We can also report that this cohort of students gained significantly higher overall grades than the students in the previous cohort, who had no access to OpenEssayist. As a system that is content free, OpenEssayist can be used to support students working in any domain that requires the writing of essays

User-modelled ambient feedback for self-regulated learning

A fundamental objective of human-computer interaction research is to make systems that are seamlessly integrated into daily life activities. Hence, the challenge is not only to make information available to people at any time, at any place, and in any form, but specifically to say the right thing at the right time in the right way. On the other hand, the proliferation of sensor technology is facilitating the scaffolding and customization of smart learning environments. This manuscript presents an ecology of resources comprising NFC, BLE and Arduino technology, orchestrated in the context of a learning environment to provide smoothly integrated feedback via ambient displays. This ecology is proposed as a suitable solution for self-regulated learning, providing support for setting goals, setting aside time to learn, tracking study time and monitoring the progress. Hereby, the ecology is described and intriguing research questions are introduced

NFC LearnTracker:Seamless support for learning with mobile and sensor technology

Lifelong learning activities are scattered along the day, in different locations and making use of multiple devices. Most of the times adults have to merge learning, work and everyday life making it difficult to have an account on how much time is devoted to learning activities and learning goals. Learning experiences are disrupted and mobile seamless learning technology provides new solutions to integrate daily life activities and learning in the same process. Hence, there is a need to provide tools that are smoothly integrated into adults’ daily life. This manuscript presents the NFC LearnTracker, a mobile tool proposing the user to immerse within his autobiography as a learner to identify successful physical learning environments, mark them with sensor tags, bind them to self-defined learning goals, keep track of the time invested on each goal with a natural interface, and monitor the learning analytics. This work implies a suitable tool for lifelong learners to bind scattered activities keeping them in a continuing learning flow. The NFC LearnTracker is released under open access licence with the aim to foster adaptation to further communities as well as to facilitate the extension to the increasing number of sensor and NFC tags existent in the market

Motivated learning with digital learning tasks: what about autonomy and structure?

Article about Motivated learning with digital learning tasks: what about autonomy and structure

Understanding the Role of Time on Task in Formative Assessment: The Case of Mathematics Learning

Mastery data derived from formative assessments constitute a rich data set in the development of student performance prediction models. The dominance of formative assessment mastery data over use intensity data such as time on task or number of clicks was the outcome of previous research by the authors in a dispositional learning analytics context. Practical implications of these findings are far reaching, contradicting current practices of developing (learning analytics based) student performance prediction models based on intensity data as central predictor variables. In this empirical follow-up study using data of 2011 students, we search for an explanation for time on task data being dominated by mastery data. We do so by investigating more general models, allowing for nonlinear, even non-monotonic, relationships between time on task and performance measures. Clustering students into subsamples, with different time on task characteristics, suggests heterogeneity of the sample to be an important cause of the nonlinear relationships with performance measures. Time on task data appear to be more sensitive to the effects of heterogeneity than mastery data, providing a further argument to prioritize formative assessment mastery data as predictor variables in the design of prediction models directed at the generation of learning feedback