## 6 Conclusions  
In this paper, we have presented a content-based method for recommending, given a query posed by a student in a course forum, other discussion threads where similar questions have been raised as well as related reinforcement activities. We have applied our methodology to a course devoted to data structure and algorithms, which is taught in the second year of different Computer Science programs. The results show that, for a given query, the recommender is able to propose both related activities and student conversations with high precision (around 80% on average). Since students usually experience the same difficulties and the queries in the discussion forums are often repeated, automatically recommending other discussions where the same query is solved will help to reduce the teaching efforts. Moreover, the \"relaxed\" strategy has been shown to be very effective in recommending activities that, while not directly answering the question posed by the student, help her deepen and expand the study of the schema or data structure underlying the problem. However, it is important to highlight as a limitation of our study the requirement for the forum contents to be accurate and reliable, as recommending erroneous content to students would be undesirable. This can only be achieved in forums moderated by the course instructors, as is in our study case. Another limitation of our study is the difficulty  
Figure 5: Evaluation results for the bags of words representation. Figure 6: Evaluation results for the key phrases representation. Figure 7: Evaluation results for the word embeddings representation. for applying conventional state-of-the-art approaches within our specific research context. Firstly, the absence of detailed student information in our dataset posed a significant limitation. Many established recommendation techniques rely on access to comprehensive user profiles, a resource we lacked. Secondly, our dataset lacks user preferences and historical interactions since the majority of students only sporadically engage in forum activities. Lastly, the volume and nature of our available data pose challenges for the implementation of certain ranking methods. Consequently, traditional recommendation methods such as Collaborative Filtering, Diversity-Aware, and Fairness-Aware ranking approaches proved unsuitable for our case. These methods typically require the ability to identify groups of similar users, provide diverse item recommendations, and access personal demographic information about the students, none of which we could fulfill. Furthermore, Learning to Rank methods, reliant on labeled data, are inapplicable due to their unavailability in our dataset. These challenges, while limiting our approach, underscore the distinctive nature of our study's context and dataset. As part of our future work, we intend to enhance our recommender system by leveraging information about the students and their progress. Specifically, with regard to recommending reinforcement activities, we will categorize these activities into different difficulty levels. This categorization will enable the recommender system to take into account the student's past performance and tailor recommendations accordingly. Additionally, we will expand our recommender system to include recommendations for online learning materials, including answers provided by ChatGPT in response to student queries. Furthermore, we aim to develop a user-friendly interface that allows students to access the recommender system effortlessly from any device and location. In terms of evaluation, our future plans involve conducting a real-world assessment. This evaluation will encompass both quantitative and qualitative aspects. Quantitatively, we will measure the average improvement of students in their final course assessments when they have used the recommender system throughout the semester. Qualitatively, we will measure the overall student satisfaction with the tool. All authors conceived the experiment(s).