An End-to-End Data Pipeline for Managing Learning Analytics

Farah, Juan Carlos; Machado, Joana; Cunha, Pedro Torres da; Ingram, Sandy; Gillet, Denis

doi:10.1109/ithet50392.2021.9759783

Cited by 3 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dataset used for our analysis was extracted from the Graasp platform using its learning analytics pipeline [7]. To analyze our data, we followed a mixedmethod approach.…”

Section: Discussionmentioning

confidence: 99%

Integrating Code Reviews into Online Lessons to Support Software Engineering Education

Farah

Spaenlehauer

Rodríguez‐Triana

et al. 2023

Lecture Notes in Networks and Systems

Self Cite

View full text Add to dashboard Cite

The use of peer code review exercises is well established in software engineering education. Nevertheless, challenges involving students' ability to perform code reviews have been identified as barriers to successfully integrating code reviews in educational settings. We have previously proposed code review notebooks as a way to address this issue. Code review notebooks resemble computational notebooks but focus on reviewing rather than executing code and can serve to introduce students to the code review process. In this study, we evaluated the effects of using a code review notebook via a case study whereby 25 university students were taught how to identify code style issues in JavaScript. Our mixed-method analysis suggests that the code review notebook format encourages students to reflect on their learning process and can result in short-term learning gains. These findings could serve instructors looking to incorporate code review exercises into their practice.

show abstract

“…The dataset used for our analysis was extracted from the Graasp platform using its learning analytics pipeline [7]. To analyze our data, we followed a mixedmethod approach.…”

Section: Discussionmentioning

confidence: 99%

Integrating Code Reviews into Online Lessons to Support Software Engineering Education

Farah

Spaenlehauer

Rodríguez‐Triana

et al. 2023

Lecture Notes in Networks and Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Figure 5 depicts the Examples phase for the chatbot condition. Interactions with the lesson were recorded using Graasp's learning analytics (LA) pipeline [35]. Upon completion of the lesson, students completed a post-questionnaire containing the instruments described in the following section.…”

Section: B Proceduresmentioning

confidence: 99%

Impersonating Chatbots in a Code Review Exercise to Teach Software Engineering Best Practices

Farah

Spaenlehauer

Sharma

et al. 2022

2022 IEEE Global Engineering Education Conference (EDUCON)

Self Cite

View full text Add to dashboard Cite

Over the past decade, the use of chatbots for educational purposes has gained considerable traction. A similar trend has been observed in social coding platforms, where automated agents support software developers with tasks such as performing code reviews. While incorporating code reviews and social coding platforms into software engineering education has been found to be beneficial, challenges such as steep learning curves and privacy considerations are barriers to their adoption. Furthermore, no study has addressed the role chatbots play in supporting code reviews as a pedagogical tool. To help address this gap, we developed an online learning application that simulates the code review features available on social coding platforms and allows instructors to interact with students using chatbot identities. We then embedded this application within a lesson on software engineering best practices and conducted a controlled in-class experiment. This experiment examined the effect that explaining content via chatbot identities had on three aspects: (i) students' perceived usability of the lesson, (ii) their engagement with the code review process, and (iii) their learning gains. While our findings show that it is feasible to simulate the code review process within an online learning platform and achieve good usability, our quantitative analysis did not yield significant differences across treatment conditions for any of the aspects considered. Nevertheless, our qualitative results suggest that students expect explicit feedback when performing this type of exercise and could thus benefit from automated replies provided by an interactive chatbot. We propose to build on our current findings to further explore this line of research in future work.

show abstract