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Example student profile report for the course "Introduction to Design and Production"

Example student profile report for the course "Introduction to Design and Production"

Source publication
Article
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Learner-centered approaches to higher education require that instructors have insight into their students' characteristics, but instructors often prepare their courses long before they have an opportunity to meet the students. To address this dilemma, we developed the institutional Student Profile Report, which informs instructors about the demogra...

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High quality feedback on assessments and deliverables is vital to student success. This pilot study sought to understand the impact of combining positive and performance-gap feedback in an immediate feedback scenario where students were provided multiple attempts to complete an assignment. 176 online undergraduate students were surveyed after compl...

Citations

... In [BJL15] the authors state that learner-centered approaches to higher education require that instructors have insight into their student?s characteristics before they start the course. ...
... The potential goals of learning analytics are diverse, including monitoring, prediction, tutoring, personalization, recommendation, etc. [MAUH12]; [KM15]. They can be categorized according to the targeted stakeholders: In [BJL15] the authors state that learner-centered approaches to higher education require that instructors have insight into their student's characteristics before they start the course. For that, they also agree that all the information about students should be given to them: demographics, enrollment history and general academic performance. ...
... 1. the influence and attitudes of the most important people in student's life, family and friends [Ren94], 2. community's involvement and attitudes [BJL15]. ...
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Book
This document that aims to reflect the necessary aspects implied into the characterisation of a student profile: pedagogical characteristics, teaching learning attitudes, description of the different situations that may reflect problems regarding a normal progress. Also the characterisation the scenario that concerns the application of educational data mining techniques. The data that a student generates while progressing on his/her studies will be synthesised and related to potential profile features. As per the SPEET project concern, the definition of an IT architecture that is aimed at dealing with such student profile characterisation is also outlined.
... Another product of learning analytics (besides actionable knowledge of learning processes) may be institution-wide datadriven dashboards and visualizations to inform teachers, advisors, and students themselves about learning behaviours and student properties (Govaerts, Verbert, Duval, & Pardo, 2012;Motz, Teague, & Shepard, 2015;Duval, 2011;Tervakari, Silius, Koro, Paukkeri, & Pirttila, 2014). In this mode, learning analytics is responsible for providing an analytical viewport to improve teaching and learning, enabling a user to become better-aware of student propensities (Verbert, Duval, Klerkx, Govaerts, & Santos, 2013). ...
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Article
To identify the ways teachers and educational systems can improve learning, researchers need to make causal inferences. Analyses of existing datasets play an important role in detecting causal patterns, but conducting experiments also plays an indispensable role in this research. In this article, we advocate for experiments to be embedded in real educational contexts, allowing researchers to test whether interventions such as a learning activity, new technology, or advising strategy elicit reliable improvements in authentic student behaviours and educational outcomes. Embedded experiments, wherein theoretically relevant variables are systematically manipulated in real learning contexts, carry strong benefits for making causal inferences, particularly when allied with the data-rich resources of contemporary e-learning environments. Toward this goal, we offer a field guide to embedded experimentation, reviewing experimental design choices, addressing ethical concerns, discussing the importance of involving teachers, and reviewing how interventions can be deployed in a variety of contexts, at a range of scales. Causal inference is a critical component of a field that aims to improve student learning; including experimentation alongside analyses of existing data in learning analytics is the most compelling way to test causal claims.
... Blanket generalizations that treat an institution's "students" as a single group are likely to be either ineffectively vague, or not applicable to all members of the student population [20]. In the classroom, post-secondary instructors find value in knowing the differentiating characteristics of the students in their classes, and tailoring instruction to accommodate their unique attributes [17]. Data-driven interventions and analytical characterizations of student behaviors should also be sensitive to the differences between students. ...
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Conference Paper
Analyses of student data in post-secondary education should be sensitive to the fact that there are many different topics of study. These different areas will interest different kinds of students, and entail different experiences and learning activities. However, it can be challenging to identify the distinct academic themes that students might pursue in higher education, where students commonly have the freedom to sample from thousands of courses in dozens of degree programs. In this paper, we describe the use of topic modeling to identify distinct themes of study and classify students according their observed course enrollments, and present possible applications of this technique for the broader field of educational data mining.
... In Arts, information of this kind is routinely sought out by instructors, curriculum committees and heads to assist with planning for instruction or broader curriculum review. Motz et al. (2015) describe their pilot of such an undertaking at U. Indiana. See also the Academic Reporting Toolkit ART 2.0 ( http://digitaleducation.umich.edu/dei/academicreportingtools/ ) at the University of Michigan. ...