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Category: CS Analytics

In learning analytics, we want to take data about our students and teachers and use this to help us understand each other and to make our learning and teaching experiences better. Rather than just collect and focus on some of the measurements that we already know about attendance, grade performance, and so on, analytics research by the CSER group looks for key indicators that we can use to measure the impact of change in pedagogical practice. Analytics is more than simple statistics, because it requires a detailed investigation of qualitative, quantitative and behavioural aspects (such as cognitive biases) to develop truly useful analytical processes.

Introducing PhD Candidate, Menasha Thilakaratne

Menasha is a full-time PhD student with us at CSER, supervised by Professor Falkner and Dr Atapattu. She has recently completed the first year Major Review milestone. Before joining the University of Adelaide, Menasha completed the Bachelor of Computer Science (Hons) from the University of Colombo, Sri Lanka, and was awarded two gold medals for […]

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ACL-SRW 2018: Automatic Detection of Cross-Disciplinary Knowledge Associations

Congratulations to Menasha Thilakaratne for having her thesis work accepted for the Student Research Workshop at ACL 2018!! M. Thilakaratne, K. Falkner and T. Atapattu. Automatic Detection of Cross-Disciplinary Knowledge Associations. Accepted for Student Research Workshop at ACL 2018. Detecting interesting, cross-disciplinary knowledge associations hidden in scientific publications can greatly assist scientists to formulate and validate scientifically […]

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Computers and Education: A comprehensive text analysis of lecture slides to generate concept maps

Final results from Thushari Atapattu’s thesis have been accepted for publication in Computers and Education: Thushari Atapattu, Katrina Falkner, and Nickolas Falkner. 2017. A comprehensive text analysis of lecture slides to generate concept maps. Comput. Educ. 115, C (December 2017), 96-113. DOI: https://doi.org/10.1016/j.compedu.2017.08.001 Current instructional methods widely support verbal learning through linear and sequential teaching […]

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ITiCSE 2017: Understanding the Effects of Intervention on Computer Science Student Behaviour in On-line Forums

Work resulting from a CSER student project, led by Daniel La Vista, has been accepted for ITICSE 2017. Daniel La Vista, Nickolas Falkner and Claudia Szabo, Understanding the Effects of Intervention on Computer Science Student Behaviour in On-line Forums. Accepted for ITiCSE 2017.  

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Latice 2017: Directing Teacher Focus in Computer Science Online Learning Environments

Work from one of our previous CSER student projects, led by Katrina Le and Hamid Tarmazdi, has been accepted for publication at Latice 2017. This work explores how text processing techniques  can be used to assist educators in identifying students who need assistance, by identifying unanswered questions, or questions that are expressing strong emotion, and […]

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LAK 2017: Discourse Analysis to Improve the Effective Engagement of MOOC Videos

Early results from our new project exploring discourse analysis in MOOC videos with the aim of improving learner engagement has been accepted for the upcoming LAK conference. This is a great result and one that reflects some of the new directions in learning analytics based on MOOC-level data analysis. Thushari Atapattu and Katrina Falkner, Discourse Analysis […]

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L@S 2017: Formal Forum Triage: Towards the Strategic Selection of Responses to Student Discussion Forums

We have commenced a new project in the area of learning analytics exploring how we can better understand how students engage in student discussion forums, with the aim of building models that describe and inform educator interventions. Early work in this new project has been accepted as a Work in Progress paper for the upcoming […]

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EDM 2016: Topic-wise classification of MOOC discussions – a visual analytics approach

Our work, funded by Google, explores how we can support personalised learning in MOOCS by automatically classifying and labelling discussion forum topics. This work helps learners navigate large amounts of discussion data, by helping them focus on the topics that are of interest to them, and helps academics by allowing them to see those topics that […]

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Learning@Scale: A Framework for Topic Generation and Labeling from MOOC Discussions

Our first results from our Google Research Grant exploring personalised learning at scale has been accepted as a Work in Progress paper at the upcoming Learning@Scale conference. We are excited to be able to share some of our preliminary results in such a forum, and to be able to share our thoughts on where this […]

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Book Chapter: An Evaluation Methodology for Concept Maps Mined from Lecture Notes: An Educational Perspective

An extended version of Thushari Atapattu’s conference paper from CSEDU has recently been published as a chapter in a special edition of the book series Communications in Computer and Information Science. The initial version of this paper was published at CSEDU in 2014, and received a nomination for Best Paper Award! T. Atapattu, K. Falkner and […]

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