Interest in Artificial Intelligence (AI) and the popularity of such courses has increased over the past few years, as a consequence higher education (HE) institutions are now offering courses in such fields. However, there is a current lack of research relating to best practice for teaching this complex topic area which encompasses both computing and mathematics knowledge. This paper outlines an initial study that set out to determine the threshold concepts within this domain through use of the One Minute Paper technique with students currently studying AI. Our results identified a number of specific models which students found troublesome including the support vector machine, recurrent neural network and the multilayer perceptron. The results indicated that topics related to deep learning were more complex for the students to fully comprehend and students may require greater in-depth tuition and alternative support mechanisms in this area in particular. Further investigation is needed to determine the heterogeneity of content delivered on courses relating to AI as well as additional studies at other HE institutions to gather more data on potential threshold concepts and the best methods to teach them.

This working group concerns the adoption of computing education (CE) in undergraduate computer science (CS) programmes. Such adoption requires both arguments sufficient to persuade our departmental colleagues and our education committees, and also curricular outlines to assist our colleagues in delivery. The goal of the group is to develop examples of both arguments and curricular outlines, drawing on any prior experience available.

Aim/Purpose: To encourage students’ engagement in peer assessments and provide students with better-quality feedback, this paper describes a technique for author-reviewer matching in peer assessment systems – a Balanced Allocation algorithm. Background: Peer assessment concerns evaluating the work of colleagues and providing feedback on their work. This process is widely applied as a learning method to involve students in the progress of their learning. However, as students have different ability levels, the efficacy of the peer feedback differs from case to case. Thus, peer assessment may not provide satisfactory results for students. In order to mitigate this issue, this paper explains and evaluates an algorithm that matches the author to a set of reviewers. The technique matches authors and reviewers based on how difficult the authors perceived the assignment to be, and the algorithm then matches the selected author to a group of reviewers who may meet the author’s needs in regard to the selected assignment. Methodology: This study used the Multiple Criteria Decision-Making methodology (MCDM) to determine a set of reviewers from among the many available options. The weighted sum method was used because the data that have been collected in user profiles are expressed in the same unit. This study produced an experimental result, examining the algorithm with a real collected dataset and mock-up dataset. In total, there were 240 students in the real dataset, and it contained self-assessment scores, peer scores, and instructor scores for the same assignment. The mock-up dataset created 1000 records for self-assessment scores. The algorithm was evaluated using focus group discussions with 29 programming students and interviews with seven programming instructors. Contribution: This paper contributes to the field in the following two ways. First, an algorithm using a MCDM methodology was proposed to match authors and reviewers in order to facilitate the peer assessment process. In addition, the algorithm used self-assessment as an initial data source to match users, rather than randomly creating reviewer – author pairs. Findings: The findings show the accurate results of the algorithm in matching three reviewers for each author. Furthermore, the algorithm was evaluated based on students’ and instructors’ perspectives. The results are very promising, as they depict a high level of satisfaction for the Balanced Allocation algorithm. Recommendations for Practitioners: We recommend instructors to consider using the Balanced Allocation algorithm to match students in peer assessments, and consequently to benefit from personalizing peer assessment based on students' needs. Recommendation for Researchers: Several MCDM methods could be expanded upon, such as the analytic hierarchy process (AHP) if different attributes are collected, or the artificial neural network (ANN) if fuzzy data is available in the user profile. Each method is suitable for special cases depending on the data available for decision-making. Impact on Society: Suitable pairing in peer assessment would increase the credibility of the peer assessment process and encourage students’ engagement in peer assessments. Future Research: The Balanced Allocation algorithm could be applied using a single group, and a peer assessment with random matching with another group may also be conducted, followed by performing a t-test to determine the impact of matching on students’ performances in the peer assessment activity.

Computing education (CE), the scientific foundation of the teaching and learning of subject matter specific to computing, has matured into a field with its own research journals and conferences as well as graduate programmes. Yet, and unlike other mature subfields of computer science (CS), it is rarely taught as part of undergraduate CS programmes. In this report, we present a gap analysis resulting from semi-structured interviews with various types of stakeholders and derive a set of arguments for teaching CE courses in undergraduate CS programmes. This analysis and the arguments highlight a number of opportunities for the discipline of CS at large, in academia, in industry, and in school education, that would be opened up with undergraduate CE courses, as well as potential barriers to implementation that will need to be overcome. We also report on the results of a Delphi process performed to elicit topics for such a course with various audiences in mind. The Delphi process yielded 19 high-level categories that encompass the subject matter CE courses should incorporate, tailored to the specific needs of their intended student audiences. This outcome underscores the extensive range of content that can be integrated into a comprehensive CE programme. Based on these two stakeholder interactions as well as a systematic literature review aiming to explore the current practices in teaching CE to undergraduate students, we develop two prototypical outlines of such a course, keeping in mind that departments may have different preferences and affordances resulting in different kinds of CE offerings. Overall, input from external stakeholders underscores the clear significance of undergraduate CE courses. We anticipate leveraging this valuable feedback to actively promote these courses on a broader scale.