Mohamed Mohamed Sacr Abdelgalil


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Works at Department of Computer and Systems Sciences
Visiting address Nodhuset, Borgarfjordsgatan 12
Postal address Institutionen för data- och systemvetenskap 164 07 Kista

About me

As a computer scientist, a practicing neurologist and a teacher my research interests areinterdisciplinary 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


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.


A selection from Stockholm University publication database
  • 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.

  • Conference Temporality matters
    2018. Mohammed Saqr, Jalal Nouri, Uno Fors. EDULEARN18, 5386-5393
  • 2018. Mohammed Saqr (et al.).

    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.

  • 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.

  • 2018. Mohammed Saqr, Jalal Nouri, Uno Fors. EDULEARN18, 7709-7716
  • 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.

Show all publications by Mohamed Mohamed Sacr Abdelgalil at Stockholm University

Last updated: February 25, 2019

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