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Mohamed Mohamed Sacr AbdelgalilAffilierad forskare

Om mig

AKA Mohammed Saqr 

As a computer scientist, a practicing neurologist and a teacher my research interests are interdisciplinary and includes Network Science and analysis, Analytics, Education, ComputerScience, Assessment, Neurology, and Psychiatry. I have done my PhD in collaborative predictivelearning analytics in a medical school. My aim was to use network indicators to produce a set ofpredictors that are relevant to the context studied, more representative of students’ activities, canbe interpreted on pedagogical grounds and offers a better understanding of the underlying learning process and success. I also investigated temporality of collaborative networks,engagement in small groups, statistical network modelling, resilience, and robustness of learningand social networks. Other research work includes migraine, depression, cerebrovascularreactivity, problem-based learning groups, and evaluation of social media in online learning. Iworked with different types of networks that include online, face-to-face and sensor networks using network analysis, network science, and machine learning.

You can find my  Google Scholar page here 

Researchgate profile  here   


My research interests span a broad spectrum of interdisciplinary areas which includes Analytics, Education, Network Sciences, Computer Science, Assessment, Pain, Neurology and Psychiatry. Currently, I am doing research in predictive learning analytics, Network Science and Analysis, Temporarily and Big Data in medicine and education.


I urval från Stockholms universitets publikationsdatabas

  • Time to focus on the temporal dimension of learning

    Mohammed Saqr, Jalal Nouri, Uno Fors. International Journal of Technology Enhanced Learning


    Time dynamics is an important element of the self-regulated learning theory. Researchers have consistently reported that students who use time and learning strategies efficiently perform better than their counterparts who don’t. Likewise, there is a sufficient volume of evidence that supports the claim that delay in performing the learning tasks (procrastination) is a consistent negative predictor of academic achievement. Although temporality is an interesting aspect of learning processes, it is yet poorly studied. Therefore, in this learning analytics study, we attempt to better understand the role of temporality measures for the prediction of academic performance by using statistical modelling and applying machine learning methods.  

    The study included four online courses over a full year duration. Students were classified as low- and high achievers. Temporality was studied on daily, weekly, course-wise and year wise. The patterns of each performance group in each period were visually plotted and compared. Correlation with the performance was done. Visualizing the activities have highlighted a certain pattern. 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 an individual 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.

    The study of temporality and how certain temporal patterns are more consistent have contributed to the production of a reasonably accurate and reproducible predictive models. 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.

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  • Using Learning Analytics to Understand and Support Collaborative Learning

    2018. Mohammed Saqr (et al.).

    Avhandling (Dok)

    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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  • Using social network analysis to understand online Problem-Based Learning and predict performance

    2018. Mohammed Saqr, Uno Fors, Jalal Nouri. PLoS ONE 13 (9)


    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.

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  • How learning analytics can early predict under-achieving students in a blended medical education course

    2017. Mohammed Saqr, Uno Fors, Matti Tedre. Medical teacher 39 (7), 757-767


    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.

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  • Bachelor thesis analytics to understand and improve quality and performance

    2020. Jalal Nouri, Ken Larsson, Mohammed Saqr. Technology, Knowledge and Learning


    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.

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  • What makes an online problem-based group successful? A learning analytics study using social network analysis

    2020. Mohammed Saqr (et al.). BMC Medical Education 20 (1)


    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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