Category: Meeting topics

Note: Room changed to Buchanan Tower 104A

Changing Students Learning Behaviour via Learning Analytics

Abstract: Three main audiences for learning analytics are institutions, instructors & course designers, and students. Our focus is on students. We take a stance that a goal of learning analytics for students should be not only to inform them about their performance, but rather clearly influence learners to modify their learning behaviour and lead to better learning outcomes. As learning sciences show, students’ learning is heavily influenced by their individual differences. Our research aims at developing understanding of how information presented in and a form of Learning Analytics visualizations affects individual students. In this talk I will elaborate on these theoretical concepts and present results of our study showing varying effect of Learning Analytics visualizations on students with different goals. Our findings highlight the methodological importance of considering individual differences and pose important implications for future design and research of learning analytics visualizations.

Dr. Marek Hatala is a Professor at the School Interactive Arts and Technology at Simon Fraser University and a Director of the Laboratory for Ontological Research. He received his PhD in Artificial Intelligence from the Technical University in Kosice (Slovakia). His research is driven by the problems arising between the computing systems and their users. The areas of his prior interests include configuration engineering design, organizational learning, semantic interoperability, ontologies and semantic web, user modeling in ubiquitous and ambient intelligence environments, and software engineering and service oriented architectures. Dr. Hatala’s current research is framed within the area of Learning Analytics. Specifically, he builds on the learning sciences to establishing the theories of effects of open learner models on learner’s motivation with the goal to improve their learning outcomes in the online learning environments.

An NLP-informed learning analytics approach for extracting and measuring aspects of argumentation

Venue: Buchanan C105C

Abstract

This paper/presentation reports on a work-in-progress and shares preliminary results for an attempt to use NLP-informed learning analytics methods to extract and measure aspects of students’ argumentation while they learn how to think and argue like scientists. The approach explored in this paper caters to aspects of deep learning and detects the flow of the argumentation directly from the structure and the composition of the language that the students use in their writings. The model integrates insights from natural language processing techniques and argumentation theory in such a way that derives the metalinguistic features of argumentation directly from the linguistic units produced in students’ written language.

On October 6th, Leah gave a short presentation on key themes and ethical issues identified by the authors of this paper..

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. doi:10.1177/000276421347936

…and we had a healthy discussion about data ethics for a learning analytics era.

It’s a complex and multidimensional area, but some universities have already made good headway in developing policy and ethical guidelines:

This week we watched this video from Educause which had various professionals discussing why measuring learning is difficult.

Some key ideas from the video that had us talking were descriptions of the process of learning as a “black box” or “magic”. We tried to bring the discussion of measuring and studying learning into the context of learning analytics.