Online discussion forums serve as dynamic environments where students and teachers collaboratively generate and utilize a wealth of content for knowledge sharing and assessment. The research involved 18 informatics graduates at the Faculty of Science at the University of Mostar, Bosnia and Herzegovina and two teachers who extracted and analyzed transcripts from an online forum, which was part of the online course "Evaluation of E-Learning Systems (EES)" held on Moodle during the winter semester of the academic year 2022/2023. The paper introduces a novel Natural Language Processing (NLP) approach to evaluating student contributions by contrasting their postings with corresponding instructional materials. Utilizing text similarity measurement, the research addresses key questions: Does the content extracted from individual student postings reflect student knowledge on a given topic? Do similarity scores align with human rankings of contribution relevance? Do students equally benefit from collaborative learning? The research evaluates the efficacy of five multilingual sentence embedding models and integrates human analysis to assess the relevance of students' contributions. Contributions of this study include the evaluation of multilingual sentence embedding models and a thorough examination of the human-perceived relevance of student contributions. The findings aim to enhance the understanding of whether this approach can effectively assess and validate the educational value of student discussions within online forums and contribute to the optimization of collaborative learning experiences.