Novice CS students can have a very wide range of starting knowledge. Bringing students to a professional level of practice requires an active process for students to develop their skills and knowledge, individually and collaboratively. Through the use of social constructivism, a theory of learning based on the benefits of collaborative social interactions between learners, these interactions allow learners to develop their knowledge by making sense of the interactions. This can take many forms, including discussing concepts, working on collaborative projects, and receiving timely and accurate feedback from their peer group and supervising instructors. The gap between what a learner is capable of doing without support and what the learner could perform with the assistance of the more knowledgeable other (MKO)---whether peer or teacher---is defined as the zone of proximal development (ZPD). Recent advances in chatbot technologies, especially those based on large language models (LLMs), and various institutional incentives naturally guide focus towards AI agents' potential as participants in collaborative social interactions. In this paper, we identify the characteristics and affordances of LLMs in how they might successfully support a computer science student through the ZPD, drawing on existing theory across key disciplines and knowledge of the characteristics of LLMs. We propose a group of models for constructive uses of this new technology, which will support social constructivism well, and provide more general guidelines for the use of LLMs.

High dropout rates in university-level computer science programmes have been consistently reported as a problem in previous works. Reasons for dropping out of CS studies are a multitude, and whether the event is a tragedy depends on the reason and the party involved. This is especially so in the context of the Finnish tertiary education model, where universities receive much of their funding based on the number of graduates, rather than on students' enrollment, attendance, or progress during studies. In this work, we report on the results of a survey sent to 416 (former) students of a Finnish computer science bachelor's programme who, according to student registry data, were likely to have dropped out of their studies. We received responses from 76 invitees and analysed them regarding the factors they self-reported as contributing to the cessation of their studies. Our results indicate that the most significant causes of dropping out are finding employment during the studies, failure to integrate well into the studies, 'personal reasons', the implementation of the studies, and a lack of support from the university. Very few students report issues relating to the atmosphere in their studies or student life (e.g., bullying, racism, harassment), parental leave, or mandatory national service (i.e., conscription or the non-military civil service alternative).

The confidence gap, that is, the difference in self-assessed confidence between genders regarding the ability to succeed in a given task, has been previously reported and studied in the context of computer science education. In this paper, we investigate the prevalence of the confidence gap during subsequent introductory and advanced Python programming courses - attended by a range of students from CS majors to non-university students taking the courses as free MOOCs - from a variety of perspectives, including student perceptions of what constitutes as 'good' or a 'bad' grade, what they predict as their own grade at the start of the course, and previous exposure to programming in various contexts. Our results provide both an updated snapshot into the evolving confidence gap, and additional data about students' beliefs on how well they are likely to perform in university-level programming courses targeted at first-year students.

Full research paper—We describe the results of a pilot study wherein we applied simple natural language generation methods to produce automated feedback for students of an online course based on student high-level progress data. Experimenting with both personalized and non-personalized feedback, we show that such feedback can be easily produced given access to even rudimentary data regarding student assignment submissions and their correctness. Our results suggest that students perceive automatically generated feedback generally positively and believe it to be useful. Our results also indicate that minor personalization and stylistic alterations in the feedback can have meaningful effects on how the feedback is interacted with and perceived. In particular, we observe that personalized feedback is perceived as being slightly easier to understand and as being better aligned with their progress. Students also felt better about the personalized feedback in comparison to non-personalized feedback. We conclude that the automated generation of personalized textual feedback shows promise as a low-threshold way of increasing student satisfaction. Further research is needed to assess the effect of different types of automated personalized feedback on student performance and behavior.

Emiel van Miltenburg, Miruna Clinciu, Ondřej Dušek, Dimitra Gkatzia, Stephanie Inglis, Leo Leppänen, Saad Mahamood, Emma Manning, Stephanie Schoch, Craig Thomson, Luou Wen. Proceedings of the 14th International Conference on Natural Language Generation. 2021.

Research on the indicators of student performance in introductory programming courses has traditionally focused on individual metrics and specific behaviors. These metrics include the amount of time and the quantity of steps such as code compilations, the number of completed assignments, and metrics that one cannot acquire from a programming environment. However, the differences in the predictive powers of different metrics and the cross-metric correlations are unclear, and thus there is no generally preferred metric of choice for examining time on task or effort in programming. In this work, we contribute to the stream of research on student time on task indicators through the analysis of a multi-source dataset that contains information about students' use of a programming environment, their use of the learning material as well as self-reported data on the amount of time that the students invested in the course and per-assignment perceptions on workload, educational value and difficulty. We compare and contrast metrics from the dataset with course performance. Our results indicate that traditionally used metrics from the same data source tend to form clusters that are highly correlated with each other, but correlate poorly with metrics from other data sources. Thus, researchers should utilize multiple data sources to gain a more accurate picture of students' learning.

Despite increasing amounts of data and ever improving natural language generation techniques, work on automated journalism is still relatively scarce.In this paper, we explore the field and challenges associated with building a journalistic natural language generation system.We present a set of requirements that should guide system design, including transparency, accuracy, modifiability and transferability.Guided by the requirements, we present a data-driven architecture for automated journalism that is largely domain and language independent.We illustrate its practical application in the production of news articles upon a user request about the 2017 Finnish municipal elections in three languages, demonstrating the successfulness of the data-driven, modular approach of the design.We then draw some lessons for future automated journalism.

We conducted two studies on the effect of visual reward mechanisms for increasing engagement with an online learning material. In the first study, we studied the effect of showing cat pictures as a reward to correct and incorrect answers to multiple choice questions, and in the second study, we created an illusion of progress using a progress bar that showed step-wise increments as students answered to the questions. Our results show the use of cat pictures as a visual reward mechanism does not significantly increase students' engagement with learning materials. At the same time, students who were shown progress bars had a statistically significant increase in the quantity of answers -- on average 88% more answers per day. However, our results also indicate that this effect declines over time, meaning that students catch up to the illusion.