Sensemaking with learning analytics visualizations: Investigating dashboard comprehension and effects on learning strategy

In the provision of just-in-time feedback, student-facing learning analytics dashboards (LADs) are meant to aid decision-making during the process of learning. Unlike summative feedback received at its conclusion, this formative feedback may help learners pivot their learning strategies while still engaged in the learning activity. To turn this feedback into actionable insights however, learners must understand LADs well enough to make accurate judgements of learning with them. For these learners, LADs could become an integral part of their self-regulatory learning strategy. This dissertation presents a multifaceted examination of learners’ sensemaking processes with LADs designed to support self-regulatory learning. The in-situ studies detailed therein examine learners’ understanding of the data visualized in LADs and the effects of this understanding on their performance-related mental models. Trace data, surveys, semi-structured in-depth qualitative interviews, and retrospective cued recall methods were used to identify why, when, and how learners used LADs to guide their learning. Learners’ qualitative accounts of their experience explained and contextualized the quantitative data collected from the observed activities. Learners preferred less complex LADs, finding them more useful and aesthetically appealing, despite lower gist recall with simpler visualizations. During an early investigation of how LADs were used to make learning judgments in situ, we observed learners’ tendency to act upon brief LAD interactions. This inspired us to operationalize gist as a form of measurement, describing learners’ ability to make sense of a LAD after a brief visual interrogation. Subsequent comparisons of the accuracy and descriptiveness of learners’ gist estimates to those of laypeople repeatedly showed that laypeople were more apt than learners to produce accurate and complete gist descriptions. This dissertation culminates in a final study examining the evolution of learners’ mental models of their performance due to repeated LAD interaction, followed by a discussion of the contextual factors that contributed to what was observed. Trends observed across this work suggest that learners were more apt to “get the gist” with LAD after repeated interaction. This dissertation contributes a novel method for evaluating learners’ interpretation of LADs, while our findings offer insight into how LADs shape learners’ sensemaking processes

A Framework for Students Profile Detection

Some of the biggest problems tackling Higher Education Institutions are students’ drop-out and academic disengagement. Physical or psychological disabilities, social-economic or academic marginalization, and emotional and affective problems, are some of the factors that can lead to it. This problematic is worsened by the shortage of educational resources, that can bridge the communication gap between the faculty staff and the affective needs of these students. This dissertation focus in the development of a framework, capable of collecting analytic data, from an array of emotions, affects and behaviours, acquired either by human observations, like a teacher in a classroom or a psychologist, or by electronic sensors and automatic analysis software, such as eye tracking devices, emotion detection through facial expression recognition software, automatic gait and posture detection, and others. The framework establishes the guidance to compile the gathered data in an ontology, to enable the extraction of patterns outliers via machine learning, which assist the profiling of students in critical situations, like disengagement, attention deficit, drop-out, and other sociological issues. Consequently, it is possible to set real-time alerts when these profiles conditions are detected, so that appropriate experts could verify the situation and employ effective procedures. The goal is that, by providing insightful real-time cognitive data and facilitating the profiling of the students’ problems, a faster personalized response to help the student is enabled, allowing academic performance improvements

Training in the technique of study

Bibliography: p. 57-66

The Contrast of Covariational Reasoning and Other Problem Solving Methods of a Calculus Student

Research has shown that covariational reasoning is indicative of success across a variety of mathematics topics, especially calculus. This paper will build on the prior research by examining one calculus student’s covariational reasoning over a multiple term teaching experiment. The tasks associated with the teaching experiment appear in a variety of mathematical forms, and in varying contexts, so that the student’s techniques for each task provide results that can be interpreted using existing frameworks. Analyzing the covariational reasoning of the student with these frameworks reveals relationships between the methods the student uses to solve tasks involving covariational reasoning and the student’s abilities to successfully solve the tasks

Mass differentiated reading skills instruction in high school

Thesis (M.Ed.)--Boston University N.B.: Page 3 Misnumbered