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  • 1.
    Nouri, Jalal
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Larsson, Ken
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Saqr, Mohammed
    University of Eastern Finland, Finland.
    Bachelor thesis analytics to understand and improve quality and performance2020In: Technology, Knowledge and Learning, ISSN 2211-1662, E-ISSN 2211-1670Article in journal (Refereed)
    Abstract [en]

    The bachelor thesis is commonly a necessary last step towards the first graduation in higher education and constitutes a central key to both further studies in higher education and employment that requires higher education degrees. Thus, completion of the thesis is a desirable outcome for individual students, academic institutions and society, and non-completion is a significant cost. Unfortunately, many academic institutions around the world experience that many thesis projects are not completed and that students struggle with the thesis process. This paper addresses this issue with the aim to, on the one hand, identify and explain why thesis projects are completed or not, and on the other hand, to predict non-completion and completion of thesis projects using machine learning algorithms. The sample for this study consisted of bachelor students’ thesis projects (n=2436) that have been started between 2010 and 2017. Data were extracted from two different data systems used to record data about thesis projects. From these systems, thesis project data were collected including variables related to both students and supervisors. Traditional statistical analysis (correlation tests, t-tests and factor analysis) was conducted in order to identify factors that influence non-completion and completion of thesis projects and several machine learning algorithms were applied in order to create a model that predicts completion and non-completion. When taking all the analysis mentioned above into account, it can be concluded with confidence that supervisors’ ability and experience play a significant role in determining the success of thesis projects, which, on the one hand, corroborates previous research.

    On the other hand, this study extends previous research by pointing out additional specific factors, such as the time supervisors take to complete thesis projects and the ratio of previously unfinished thesis projects. It can also be concluded that the academic title of the supervisor, which was one of the variables studied, did not constitute a factor for completing thesis projects. One of the more novel contributions of this study stems from the application of machine learning algorithms that were used in order to – reasonably accurately – predict thesis completion/non-completion. Such predictive models offer the opportunity to support a more optimal matching of students and supervisors.

  • 2.
    Saqr, Mohammed
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Using Learning Analytics to Understand and Support Collaborative Learning2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Learning analytics (LA) is a rapidly evolving research discipline that uses insights generated from data analysis to support learners and optimize both the learning process and learning environment. LA is driven by the availability of massive data records regarding learners, the revolutionary development of big data methods, cheaper and faster hardware, and the successful implementation of analytics in other domains. The prime objective of this thesis is to investigate the potential of learning analytics in understanding learning patterns and learners’ behavior in collaborative learning environments with the premise of improving teaching and learning. More specifically, the research questions comprise: How can learning analytics and social network analysis (SNA) reliably predict students’ performance using contextual, theory-based indicators, and how can social network analysis be used to analyze online collaborative learning, guide a data-driven intervention, and evaluate it. The research methods followed a structured process of data collection, preparation, exploration, and analysis. Students’ data were collected from the online learning management system using custom plugins and database queries. Data from different sources were assembled and verified, and corrupted records were eliminated. Descriptive statistics and visualizations were performed to summarize the data, plot variables’ distributions, and detect interesting patterns. Exploratory statistical analysis was conducted to explore trends and potential predictors, and to guide the selection of analysis methods. Using insights from these steps, different statistical and machine learning methods were applied to analyze the data. The results indicate that a reasonable number of underachieving students could be predicted early using self-regulation, engagement, and collaborative learning indicators. Visualizing collaborative learning interactions using SNA offered an easy-to-interpret overview of the status of collaboration, and mapped the roles played by teachers and students. SNA-based monitoring helped improve collaborative learning through a data-driven intervention. The combination of SNA visualization and mathematical analysis of students’ position, connectedness, and role in collaboration was found to help predict students’ performance with reasonable accuracy. The early prediction of performance offers a clear opportunity for the implementation of effective remedial strategies and facilitates improvements in learning. Furthermore, using SNA to monitor and improve collaborative learning could contribute to better learning and teaching.

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  • 3.
    Saqr, Mohammed
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. University of Eastern Finland, Finland.
    Alamro, Ahmad
    The role of social network analysis as a learning analytics tool in online problem based learning2019In: BMC Medical Education, ISSN 1472-6920, E-ISSN 1472-6920, Vol. 19, article id 160Article in journal (Refereed)
    Abstract [en]

    Background: Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online (Problem Based Learning) PBL in particular. Using SNA to study students' positions in information exchange networks, communicational activities, and interactions, we can broaden our understanding of the process of PBL, evaluate the significance of each participant role and learn how interactions can affect academic performance. The aim of this study was to study how SNA visual and mathematical analysis can be sued to investigate online PBL, furthermore, to see if students' position and interaction parameters are associated with better performance.

    Methods: This study involved 135 students and 15 teachers in 15 PBL groups in the course of growth and development at Qassim University. The course uses blended PBL as the teaching method. All interaction data were extracted from the learning management system, analyzed with SNA visual and mathematical techniques on the individual student and group level, centrality measures were calculated, and participants' roles were mapped. Correlation among variables was performed using the non-parametric Spearman rank correlation test.

    Results: The course had 2620 online interactions, mostly from students to students (89%), students to teacher interactions were 4.9%, and teacher to student interactions were 6.15%. Results have shown that SNA visual analysis can precisely map each PBL group and the level of activity within the group as well as outline the interactions among group participants, identify the isolated and the active students (leaders and facilitators) and evaluate the role of the tutor. Statistical analysis has shown that students' level of activity (outdegree r(s)(133) = 0.27, p = 0.01), interaction with tutors (r(s) (133) = 0.22, p = 0.02) are positively correlated with academic performance.

    Conclusions: Social network analysis is a practical method that can reliably monitor the interactions in an online PBL environment. Using SNA could reveal important information about the course, the group, and individual students. The insights generated by SNA may be useful in the context of learning analytics to help monitor students' activity.

  • 4.
    Saqr, Mohammed
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Fors, Uno
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Nouri, Jalal
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Using social network analysis to understand online Problem-Based Learning and predict performance2018In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 13, no 9, article id e0203590Article in journal (Refereed)
    Abstract [en]

    Social network analysis (SNA) may be of significant value in studying online collaborative learning. SNA can enhance our understanding of the collaborative process, predict the under-achievers by means of learning analytics and uncover the role dynamics of learners and teachers alike. As such, it constitutes an obvious opportunity to improve learning, inform teachers and stakeholders.  Besides, it can facilitate data-driven support services for students.

    This study included four courses in Qassim University. Online interaction data were collected and processed following a standard data mining technique. The SNA parameters relevant to knowledge sharing and construction were calculated on the individual and the group level. The analysis included quantitative network analysis and visualizatization, correlation tests as well as predictive and explanatory regression models.

    Our results showed a consistent moderate to strong positive correlation between performance, interaction parameters and students’ centrality measures across all the studied courses, regardless of the subject matter. In each of the studied courses, students with stronger ties to prominent peers (better social capital) in small interactive and cohesive groups tended to do better. The results of correlation tests were confirmed using regression tests, which were validated using a next year dataset. Using SNA indicators, we were able to classify students according to achievement with a high accuracy (93.3%). This demonstrates the possibility of using interaction data to predict underachievers with a reasonable reliability, which is an obvious opportunity for intervention and support.

  • 5.
    Saqr, Mohammed
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Qassim University, Kingdom of Saudi Arabia.
    Fors, Uno
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Tedre, Matti
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    How learning analytics can early predict under-achieving students in a blended medical education course2017In: Medical teacher, ISSN 0142-159X, E-ISSN 1466-187X, Vol. 39, no 7, p. 757-767Article in journal (Refereed)
    Abstract [en]

    Aim: Learning analytics (LA) is an emerging discipline that aims at analyzing students' online data in order to improve the learning process and optimize learning environments. It has yet un-explored potential in the field of medical education, which can be particularly helpful in the early prediction and identification of under-achieving students. The aim of this study was to identify quantitative markers collected from students' online activities that may correlate with students' final performance and to investigate the possibility of predicting the potential risk of a student failing or dropping out of a course.Methods: This study included 133 students enrolled in a blended medical course where they were free to use the learning management system at their will. We extracted their online activity data using database queries and Moodle plugins. Data included logins, views, forums, time, formative assessment, and communications at different points of time. Five engagement indicators were also calculated which would reflect self-regulation and engagement. Students who scored below 5% over the passing mark were considered to be potentially at risk of under-achieving.Results: At the end of the course, we were able to predict the final grade with 63.5% accuracy, and identify 53.9% of at-risk students. Using a binary logistic model improved prediction to 80.8%. Using data recorded until the mid-course, prediction accuracy was 42.3%. The most important predictors were factors reflecting engagement of the students and the consistency of using the online resources.Conclusions: The analysis of students' online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention.

  • 6.
    Saqr, Mohammed
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Nouri, Jalal
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Fors, Uno
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Temporality matters: A learning analytics study of the patterns of interactions and its relation to performance2018In: EDULEARN18: Proceedings, The International Academy of Technology, Education and Development, 2018, p. 5386-5393Conference paper (Refereed)
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  • 7.
    Saqr, Mohammed
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Nouri, Jalal
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Fors, Uno
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Time to focus on the temporal dimension of learning: A learning analytics study of the temporal patterns of students’ interactions and self-regulation2019In: International Journal of Technology Enhanced Learning, ISSN 1753-5255, E-ISSN 1753-5263, Vol. 11, no 4, p. 398-412Article in journal (Refereed)
    Abstract [en]

    In this learning analytics study, we attempt to understand the role of temporality measures for the prediction of academic performance. The study included four online courses over a full-year duration. Temporality was studied on daily, weekly, course-wise and year-wise. Visualising the activities has highlighted certain patterns. On the week level, early participation was a consistent predictor of high achievement. This finding was consistent from course to course and during most periods of the year. On course level, high achievers were also likely to participate early and consistently. With a focus on temporal measures, we were able to predict high achievers with reasonable accuracy in each course. These findings highlight the idea that temporality dimension is a significant source of information about learning patterns and has the potential to inform educators about students’ activities and to improve the accuracy and reproducibility of predicting students’ performance.

  • 8.
    Saqr, Mohammed
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Nouri, Jalal
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Fors, Uno
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    What shapes the communities of learners in a medical school2018In: EDULEARN18: Proceedings, The International Academy of Technology, Education and Development, 2018, p. 7709-7716Conference paper (Refereed)
    Download full text (pdf)
    fulltext
  • 9.
    Saqr, Mohammed
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. University of Eastern Finland, Finland.
    Nouri, Jalal
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Vartiainen, Henriikka
    Malmberg, Jonna
    What makes an online problem-based group successful? A learning analytics study using social network analysis2020In: BMC Medical Education, ISSN 1472-6920, E-ISSN 1472-6920, Vol. 20, no 1, article id 80Article in journal (Refereed)
    Abstract [en]

    Background Although there is a wealth of research focusing on PBL, most studies employ self-reports, surveys, and interviews as data collection methods and have an exclusive focus on students. There is little research that has studied interactivity in online PBL settings through the lens of Social Network Analysis (SNA) to explore both student and teacher factors that could help monitor and possibly proactively support PBL groups. This study adopts SNA to investigate how groups, tutors and individual student's interactivity variables correlate with group performance and whether the interactivity variables could be used to predict group performance. Methods We do so by analyzing 60 groups' work in 12 courses in dental education (598 students). The interaction data were extracted from a Moodle-based online learning platform to construct the aggregate networks of each group. SNA variables were calculated at the group level, students' level and tutor's level. We then performed correlation tests and multiple regression analysis using SNA measures and performance data. Results The findings demonstrate that certain interaction variables are indicative of a well-performing group; particularly the quantity of interactions, active and reciprocal interactions among students, and group cohesion measures (transitivity and reciprocity). A more dominating role for teachers may be a negative sign of group performance. Finally, a stepwise multiple regression test demonstrated that SNA centrality measures could be used to predict group performance. A significant equation was found, F (4, 55) = 49.1, p < 0.01, with an R2 of 0.76. Tutor Eigen centrality, user count, and centralization outdegree were all statistically significant and negative. However, reciprocity in the group was a positive predictor of group improvement. Conclusions The findings of this study emphasized the importance of interactions, equal participation and inclusion of all group members, and reciprocity and group cohesion as predictors of a functioning group. Furthermore, SNA could be used to monitor online PBL groups, identify important quantitative data that helps predict and potentially support groups to function and co-regulate, which would improve the outcome of interacting groups in PBL. The information offered by SNA requires relatively little effort to analyze and could help educators get valuable insights about their groups and individual collaborators.

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