︎     Collaborative Interventions for Novices to Learn Programming

This research focuses on studying collaborative learning interactions that may facilitate the construction of pedagogically rich programming learning experiences for novices. We are particularly interested in understanding how an individual’s programming learning behaviour unfolds in a social learning context. We intend to foster novices’ learning behaviour by designing interventions for programming platforms that help mediate collaborative interactions amongst learners for a positive learning outcome and thereby also enhancing their programming learning experience.

In the first research project, we explore assisting students’ social learning regulation and their learning motivation, in an online introductory programming classroom. Through the design and evaluation of a collaborative Jupyter Notebook extension we inform the design of technological interventions to foster novices’ programming regulation.


 ︎︎︎   In print

︎    Good Research Practices in HCI

To increase research efficiency and enable robust scientific findings, researchers must adopt transparent and open research practices, allowing for the replication and reproducibility of their studies and allowing other researchers to extend and build further research from their studies. This is especially important in the field of Human Computer Interaction (HCI), which deals with a broad range of human experiences and activities. As such the  importance of  ethical research considerations, open science initiatives and transparent research practices are paramount in the field and should be promoted. 

Changes in Research Ethics, Openness, and Transparency in Empirical Studies between CHI 2017 and CHI 2022

In this first attempt to understand the extent to which the HCI community is committed to research ethics, openness and transparency of empirical studies, we focused on analysing papers published at the ACM CHI conference, as it is a prestigious and flagship HCI venue. We developed a set of 45 criteria and evaluated a sample of papers from CHI 2017 and CHI 2022. Although there was overall improvement, we identified specific areas that could benefit from further attention by the HCI community. Additionally, we developed a proof-of-concept screening system to explore the potential of assisting the verification process.

Contributors: Kavous Salehzadeh Niksirat, Lahari Goswami, Pooja Rao, James Tyler, Alessandro Silacci, Sadiq Aliyu, Annika Aebli, Chat Wacharamanotham, Mauro Cherubini


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︎︎︎    Video Presentation : ︎