| 1. |
- Bermo, Mohammed, et al.
(författare)
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Utility of SPECT Functional Neuroimaging of Pain
- 2021
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Ingår i: Frontiers in Psychiatry. - : Frontiers Media SA. - 1664-0640. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Functional neuroimaging modalities vary in spatial and temporal resolution. One major limitation of most functional neuroimaging modalities is that only neural activation taking place inside the scanner can be imaged. This limitation makes functional neuroimaging in many clinical scenarios extremely difficult or impossible. The most commonly used radiopharmaceutical in Single Photon Emission Tomography (SPECT) functional brain imaging is Technetium 99 m-labeled Ethyl Cysteinate Dimer (ECD). ECD is a lipophilic compound with unique pharmacodynamics. It crosses the blood brain barrier and has high first pass extraction by the neurons proportional to regional brain perfusion at the time of injection. It reaches peak activity in the brain 1 min after injection and is then slowly cleared from the brain following a biexponential mode. This allows for a practical imaging window of 1 or 2 h after injection. In other words, it freezes a snapshot of brain perfusion at the time of injection that is kept and can be imaged later. This unique feature allows for designing functional brain imaging studies that do not require the patient to be inside the scanner at the time of brain activation. Functional brain imaging during severe burn wound care is an example that has been extensively studied using this technique. Not only does SPECT allow for imaging of brain activity under extreme pain conditions in clinical settings, but it also allows for imaging of brain activity modulation in response to analgesic maneuvers whether pharmacologic or non-traditional such as using virtual reality analgesia. Together with its utility in extreme situations, SPECTS is also helpful in investigating brain activation under typical pain conditions such as experimental controlled pain and chronic pain syndromes.
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| 2. |
- Abdelgalil, Mohammed Saqr, 1975-, et al.
(författare)
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Idiographic learning analytics : A single student (N=1) approach using psychological networks
- 2021
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS. ; , s. 16-22
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Konferensbidrag (refereegranskat)abstract
- Recent findings in the field of learning analytics have brought to our attention that conclusions drawn from cross-sectional group-level data may not capture the dynamic processes that unfold within each individual learner. In this light, idiographic methods have started to gain grounds in many fields as a possible solution to examine students' behavior at the individual level by using several data points from each learner to create person-specific insights. In this study, we introduce such novel methods to the learning analytics field by exploring the possible potentials that one can gain from zooming in on the fine-grained dynamics of a single student. Specifically, we make use of Gaussian Graphical Models -an emerging trend in network science- to analyze a single student's dispositions and devise insights specific to him/her. The results of our study revealed that the student under examination may be in need to learn better self-regulation techniques regarding reflection and planning.
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| 3. |
- Abdelgalil, Mohammed Saqr, et al.
(författare)
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The longitudinal trajectories of online engagement over a full program
- 2021
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Ingår i: Computers and education. - : Elsevier BV. - 0360-1315 .- 1873-782X. ; 175
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Tidskriftsartikel (refereegranskat)abstract
- Student engagement has a trajectory (a timeline) that unfolds over time and can be shaped by different factors including learners' motivation, school conditions, and the nature of learning tasks. Such factors may result in either a stable, declining or fluctuating engagement trajectory. While research on online engagement is abundant, most authors have examined student engagement in a single course or two. Little research has been devoted to studying online longitudinal engagement, i.e., the evolution of student engagement over a full educational program. This learning analytics study examines the engagement states (sequences, successions, stability, and transitions) of 106 students in 1396 course enrollments over a full program. All data of students enrolled in the academic year 2014-2015, and their subsequent data in 2015-2016, 2016-2017, and 2017-2018 (15 courses) were collected. The engagement states were clustered using Hidden Markov Models (HMM) to uncover the hidden engagement trajectories which resulted in a mostly-engaged (33% of students), an intermediate (39.6%), and a troubled (27.4%) trajectory. The mostly-engaged trajectory was stable with infrequent changes, scored the highest, and was less likely to drop out. The troubled trajectory showed early disengagement, frequent dropouts and scored the lowest grades. The results of our study show how to identify early program disengagement (activities within the third decile) and when students may drop out (first year and early second year).
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| 4. |
- Abdelgalil, Mohammed Saqr, et al.
(författare)
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Toward self big data
- 2021
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Ingår i: International Journal of Health Sciences (IJHS). - : QASSIM UNIV, COLL MEDICINE. - 1658-3639. ; 15:5, s. 1-2
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Tidskriftsartikel (refereegranskat)
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| 5. |
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| 6. |
- Apiola, Mikko, et al.
(författare)
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A Scientometric Journey Through the FIE Bookshelf : 1982-2020
- 2021
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Ingår i: 2021 IEEE Frontiers in Education Conference (FIE). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665438513
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Konferensbidrag (refereegranskat)abstract
- IEEE/ASEE Frontiers in Education turned 50 at the 2020 virtual conference in Uppsala, Sweden. This paper presents an historical retrospective on the first 50 years of the conference from a scientometric perspective. That is to say, we explore the evolution of the conference in terms of prolific authors, communities of co-authorship, clusters of topics, and internationalization, as the conference transcended its largely provincial US roots to become a truly international forum through which to explore the frontiers of educational research and practice. The paper demonstrates the significance of FIE for a core of 30% repeat authors, many of whom have been members of the community and regular contributors for more than 20 years. It also demonstrates that internal citation rates are low, and that the co-authoring networks remain strongly dominated by clusters around highly prolific authors from a few well known US institutions. We conclude that FIE has truly come of age as an international venue for publishing high quality research and practice papers, while at the same time urging members of the community to be aware of prior work published at FIE, and to consider using it more actively as a foundation for future advances in the field.
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| 7. |
- Apiola, Mikko, et al.
(författare)
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A Scientometric Journey Through the FIE Bookshelf : 1982-2020
- 2021
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Ingår i: 2021 IEEE frontiers in education conference (FIE 2021). - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- IEEE/ASEE Frontiers in Education turned 50 at the 2020 virtual conference in Uppsala, Sweden. This paper presents an historical retrospective on the first 50 years of the conference from a scientometric perspective. That is to say, we explore the evolution of the conference in terms of prolific authors, communities of co-authorship, clusters of topics, and internationalization, as the conference transcended its largely provincial US roots to become a truly international forum through which to explore the frontiers of educational research and practice. The paper demonstrates the significance of FIE for a core of 30% repeat authors, many of whom have been members of the community and regular contributors for more than 20 years. It also demonstrates that internal citation rates are low, and that the co-authoring networks remain strongly dominated by clusters around highly prolific authors from a few well known US institutions. We conclude that FIE has truly come of age as an international venue for publishing high quality research and practice papers, while at the same time urging members of the community to be aware of prior work published at FIE, and to consider using it more actively as a foundation for future advances in the field.
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| 8. |
- Apiola, Mikko, et al.
(författare)
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Exploring the Past, Present and Future of Computing Education Research: An Introduction
- 2023
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Ingår i: Past, Present and Future of Computing Education Research. - : Springer Nature. ; , s. 1-7
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Bokkapitel (refereegranskat)abstract
- This chapter is an introduction to the book “Past, Present and Future of Computing Education Research: A Global Perspective.” This book uses a mixture of scientometrics, meta-research and case studies to offer a new view about the evolution and current state of computing education research (CER) as a field of science. In its 21 chapters, this book presents new insights of authors, author communities, publication venues, topics of research, and of regional initiatives and topical communities of CER. This chapter presents an overview of the contents of this book.
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| 9. |
- Apiola, Mikko, et al.
(författare)
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From a National Meeting to an International Conference : A Scientometric Case Study of a Finnish Computing Education Conference
- 2022
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 10, s. 66576-66588
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Tidskriftsartikel (refereegranskat)abstract
- Computerisation and digitalisation are shaping the world in fundamental and unpredictable ways, which highlights the importance of computing education research (CER). As part of understanding the roots of CER, it is crucial to investigate the evolution of CER as a research discipline. In this paper we present a case study of a Finnish CER conference called Koli Calling, which was launched in 2001, and which has become a central publication venue of CER. We use data from 2001 to 2020, and investigate the evolution of Koli Calling's scholarly communities and zoom in on it's publication habits and internalisation process. We explore the narrative of the development and scholarly agenda behind changes in the conference submission categories from the perspective of some of the conference chairs over the years. We then take a qualitative perspective, analysing the conference publications based on a comprehensive bibliometric analysis. The outcomes include classification of important research clusters of authors in the community of conference contributors. Interestingly, we find traces of important events in the historical development of CER. In particular, we find clusters emerging from specific research capacity building initiatives and we can trace how these connect research spanning the world CER community from Finland to Sweden and then further to the USA, Australia and New Zealand. This paper makes a strategic contribution to the evolution of CER as a research discipline, from the perspective of one central event and publication venue, providing a broad perspective on the role of the conference in connecting research clusters and establishing an international research community. This work contributes insights to researchers in one specific CER community and how they shape the future of computing education
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| 10. |
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| 11. |
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| 12. |
- Hrastinski, Stefan, Professor, 1980-, et al.
(författare)
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Examining the Development of K-12 Students' Cognitive Presence over Time : The Case of Online Mathematics Tutoring
- 2023
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Ingår i: ONLINE LEARNING. - : The Online Learning Consortium. - 2472-5749. ; 27:3, s. 252-270
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we focus on the cognitive presence element of the Community of Inquiry (CoI) framework. Cognitive presence consists of four categories: Triggering Event, Exploration, Integration, and Resolution. These categories have been described as phases following an idealized logical sequence, although the phases should not be seen as immutable. Few studies have empirically examined how the four categories develop over time during the inquiry process. This article uses learning analytics methods to study transitions between the categories in K-12 online mathematics tutoring. It was statistically most probable that the tutoring sessions started with Triggering Event (95%) and then transitioned to Exploration (51%). The transitions from Exploration to Integration (18%) and Integration to Resolution (21%) achieved statistical significance but were less likely. In fact, it was more likely that the tutoring sessions transitioned from Integration to Exploration (39%) and Resolution to Exploration (36%). In conclusion, the findings suggest that the idealized logical sequence is evident in the data but that other transitions occur as well; especially Exploration recurs throughout the sessions. It seems challenging for students to reach the Integration and Resolution categories. As the CoI framework is commonly adopted in practice, it is important that tutors and educators understand that the categories of cognitive presence will often not play out in idealized ways, underlining their role in supporting how the inquiry process unfolds. In order to gain an improved understanding of the inquiry process, future research is suggested to investigate how the presences and categories of the CoI framework develop over time in different educational settings.
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| 13. |
- Jovanović, Jelena, et al.
(författare)
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Students matter the most in learning analytics : The effects of internal and instructional conditions in predicting academic success
- 2021
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Ingår i: Computers and education. - : Pergamon. - 0360-1315 .- 1873-782X. ; 172, s. 104251-
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Tidskriftsartikel (refereegranskat)abstract
- Predictive modelling of academic success and retention has been a key research theme in Learning Analytics. While the initial work on predictive modelling was focused on the development of general predictive models, portable across different learning settings, later studies demonstrated the drawbacks of not considering the specificities of course design and disciplinary context. This study builds on the methods and findings of related earlier studies to further explore factors predictive of learners' academic success in blended learning. In doing so, it differentiates itself by (i) relying on a larger and homogeneous course sample (15 courses, 50 course offerings in total), and (ii) considering both internal and external conditions as factors affecting the learning process. We apply mixed effect linear regression models, to examine: i) to what extent indicators of students' online learning behaviour can explain the variability in the final grades, and ii) to what extent that variability is attributable to the course and students' internal conditions, not captured by the logged data. Having examined different types of behaviour indicators (e.g., indicators of the overall activity level, those indicative of regularity of study, etc), we found little difference, if any, in their predictive power. Our results further indicate that a low proportion of variance is explained by the behaviour-based indicators, while a significant portion of variability stems from the learners' internal conditions. Hence, when variability in external conditions is largely controlled for (the same institution, discipline, and nominal pedagogical model), students' internal state is the key predictor of their course performance.
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| 14. |
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| 15. |
- López-Pernas, Sonsoles, et al.
(författare)
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Putting It All Together:Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming
- 2021
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Ingår i: Sustainability. - : MDPI AG. - 2071-1050. ; 13:9, s. 4825-4843
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Tidskriftsartikel (refereegranskat)abstract
- Learning programming is a complex and challenging task for many students. It involves both understanding theoretical concepts and acquiring practical skills. Hence, analyzing learners’ data from online learning environments alone fails to capture the full breadth of students’ actions if part of their learning process takes place elsewhere. Moreover, existing studies on learning analytics applied to programming education have mainly relied on frequency analysis to classify students according to their approach to programming or to predict academic achievement. However, frequency analysis provides limited insights into the individual time-related characteristics of the learning process. The current study examines students’ strategies when learning programming, combining data from the learning management system and from an automated assessment tool used to support students while solving the programming assignments. The study included the data of 292 engineering students (228 men and 64 women, aged 20–26) from the two aforementioned sources. To gain an in-depth understanding of students’ learning process as well as of the types of learners, we used learning analytics methods that account for the temporal order of learning actions. Our results show that students have special preferences for specific learning resources when learning programming, namely, slides that support search, and copy and paste. We also found that videos are relatively less consumed by students, especially while working on programming assignments. Lastly, students resort to course forums to seek help only when they struggle.
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| 16. |
- Nouri, Jalal, et al.
(författare)
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Bachelor Thesis Analytics : Using Machine Learning to Predict Dropout and Identify Performance Factors
- 2019
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Ingår i: International journal of learning analytics and artificial intelligence for education. - : International Association of Online Engineering (IAOE). - 2706-7564. ; 1:1, s. 116-131
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 17. |
- Nouri, Jalal, et al.
(författare)
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Bachelor thesis analytics to understand and improve quality and performance
- 2020
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Ingår i: Technology, Knowledge and Learning. - 2211-1662 .- 2211-1670.
-
Tidskriftsartikel (refereegranskat)abstract
- 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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| 18. |
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| 19. |
- Nouri, Jalal, et al.
(författare)
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Efforts in Europe for Data-Driven Improvement of Education – A review of learning analytics research in seven countries
- 2019
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Ingår i: International journal of learning analytics and artificial intelligence for education. - : International Association of Online Engineering (IAOE). - 2706-7564. ; 1:1, s. 8-27
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Tidskriftsartikel (refereegranskat)abstract
- Information and communication technologies are increasingly mediating learning and teaching practices as well as how educational institutions are handling their administrative work. As such, students and teachers are leaving large amounts of digital footprints and traces in various educational apps and learning management platforms, and educational administrators register various processes and outcomes in digital administrative systems. It is against such a background we in recent years have seen the emergence of the fast-growing and multi-disciplinary field of learning analytics. In this paper, we examine the research efforts that have been conducted in the field of learning analytics in Austria, Denmark, Finland, Norway, Germany, Spain, and Sweden. More specifically, we report on developed national policies, infrastructures and competence centers, as well as major research projects and developed research strands within the selected countries. The main conclusions of this paper are that the work of researchers around Europe has not led to national adoption or European level strategies for learning analytics. Furthermore, most countries have not established national policies for learners’ data or guidelines that govern the ethical usage of data in research or education. We also conclude, that learning analytics research on pre-university level to high extent have been overlooked. In the same vein, learning analytics has not received enough focus form national and European national bodies. Such funding is necessary for taking steps towards data-driven development of education.
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| 20. |
- Nouri, Jalal, et al.
(författare)
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Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning
- 2019
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Ingår i: Transforming Learning with Meaningful Technologies. - Cham : Springer Nature. - 9783030297350 - 9783030297367 ; , s. 28-39
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Konferensbidrag (refereegranskat)abstract
- The master thesis is the last formal step in most universities around the world. However, all students do not finish their master thesis. Thus, it is reasonable to assume that the non-completion of the master thesis should be viewed as a substantial problem that requires serious attention and proactive planning. This learning analytics study aims to understand better factors that influence completion and non-completion of master thesis projects. More specifically, we ask: which student and supervisor factors influence completion and non-completion of master thesis? Can we predict completion and non-completion of master thesis using such variables in order to optimise the matching of supervisors and students? To answer the research questions, we extracted data about supervisors and students from two thesis management systems which record large amounts of data related to the thesis process. The sample used was 755 master thesis projects supervised by 109 teachers. By applying traditional statistical methods (descriptive statistics, correlation tests and independent sample t-tests), as well as machine learning algorithms, we identify five central factors that can accurately predict master thesis completion and non-completion. Besides the identified predictors that explain master thesis completion and non-completion, this study contributes to demonstrating how educational data and learning analytics can produce actionable data-driven insights. In this case, insights that can be utilised to inform and optimise how supervisors and students are matched and to stimulate targeted training and capacity building of supervisors.
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| 21. |
- Nouri, Jalal, et al.
(författare)
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Predicting performance of students in a flipped classroom using machine learning : towards automated data-driven formative feedback
- 2019
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Ingår i: Journal of Systemics, Cybernetics and Informatics. - 1690-4532 .- 1690-4524. ; 17:4, s. 17-21
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Tidskriftsartikel (refereegranskat)abstract
- Learning analytics (LA) is a relatively new research discipline that uses data to try to improve learning, optimizing the learning process and develop the environment in which learning occurs. One of the objectives of LA is to monitor students activities and early predict performance to improve retention, offer personalized feedback and facilitate the provision of support to the students. Flipped classroom is one of the pedagogical methods that find strength in the combination of physical and digital environments i.e. blended learning environments. Flipped classroom often make use of learning management systems in which video-recorded lectures and digital material is made available, which thus generates data about students interactions with these materials. In this paper, we report on a study conducted with focus on a flipped learning course in research methodology. Based on data regarding how students interact with course material (video recorded lectures and reading material), how they interact with teachers and other peers in discussion forums, and how they perform on a digital assessment (digital quiz), we apply machine learning methods (i.e. Neural Networks, Nave Bayes, Random Forest, kNN, and Logistic regression) in order to predict students overall performance on the course. The final predictive model that we present in this paper could with fairly high accuracy predict low- and high achievers in the course based on activity and early assessment data. Using this approach, we are given opportunities to develop learning management systems that provide automatic datadriven formative feedback that can help students to selfregulate as well as inform teachers where and how to intervene and scaffold students.
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| 22. |
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| 23. |
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| 24. |
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| 25. |
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| 26. |
- Saqr Abdelgalil, Mohammed, 1975-
(författare)
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Big data and the emerging ethical challenges
- 2017
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Ingår i: International Journal of Health Sciences. - : Qassim University. - 1658-3639. ; 11:4
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Tidskriftsartikel (refereegranskat)
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| 27. |
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| 28. |
- Saqr Abdelgalil, Mohammed, 1975-, et al.
(författare)
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Using psychological networks to reveal the interplay between foreign language students' self-regulated learning tactics
- 2021
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Ingår i: Harnessing the Potentials of Technology to Support Self-Directed Language Learning in Online Learning Settings (STELLA 2020). - : CEUR. ; , s. 12-23
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Konferensbidrag (refereegranskat)abstract
- Students' ability to self-regulate their individual and collaborative learning activities while performing challenging academic writing tasks is instrumental for their academic success. Presently, the majority of such learning activities often occur in computer-supported collaborative learning (CSCL) settings, in which students generate digital learner data. Examining this data may provide valuable insights into their self-regulated learning (SRL) behaviours. Such an understanding is important for educators to provide adequate support. Recent advances in the fields of learning analytics (LA) and SRL offer new ways to analyse such data and understand students' dynamic SRL processes. This study uses a novel psychological network method, i.e., Gaussian Graphical Models, to model the interactions between the students' SRL tactics and how they influence language learning in a CSCL setting for academic writing. The data for this study was generated by first-year foreign language students (n=119) who used a Facebook group as a collaborative space for peer review in an academic writing course. The theoretical lens of strategic self-regulated language learning was applied. The findings show a strong connection between the following tactics: writing text, social bonding and acknowledging. Strong connections between students' reflective activities and their application of feedback, as well as between acculturating, organising and using resources were also identified. Centrality measures showed that acculturating is most strongly connected to all other tactics, followed by acknowledging and social bonding. Expected influence centrality measures showed acculturating and social interactions to be strong influencers. Students' academic performance and their use of tactics showed little correlation.
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| 29. |
- Saqr, Mohammed, et al.
(författare)
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A Learning Analytics Study of the Effect of Group Size on Social Dynamics and Performance in Online Collaborative Learning
- 2019
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Ingår i: Transforming Learning with Meaningful Technologies. - Cham : Springer. - 9783030297350 - 9783030297367 ; , s. 466-479
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Konferensbidrag (refereegranskat)abstract
- Effective collaborative learning is rarely a spontaneous phenomenon. In fact, it requires that a set of conditions are met. Among these central conditions are group formation, size and interaction dynamics. While previous research has demonstrated that size might have detrimental effects on collaborative learning, few have examined how social dynamics develop depending on group size. This learning analytics paper reports on a study that asks: How is group size affecting social dynamics and performance of collaborating students? In contrast to previous research that was mainly qualitative and assessed a limited sample size, our study included 23,979 interactions from 20 courses, 114 groups and 974 students and the group size ranged from 7 to 15 in the context of online problem-based learning. To capture the social dynamics, we applied social network analysis for the study of how group size affects collaborative learning. In general, we conclude that larger groups are associated with decreased performance of individual students, poorer and less diverse social interactions. A high group size led to a less cohesive group, with less efficient communication and less information exchange among members. Large groups may facilitate isolation and inactivity of some students, which is contrary to what collaborative learning is about.
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| 30. |
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| 31. |
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| 32. |
- Saqr, Mohammed, et al.
(författare)
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Capturing the participation and social dimensions of computer-supported collaborative learning through social network analysis : which method and measures matter?
- 2020
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Ingår i: International Journal of Computer-Supported Collaborative Learning. - : Springer Nature. - 1556-1607 .- 1556-1615. ; 15:2, s. 227-248
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Tidskriftsartikel (refereegranskat)abstract
- The increasing use of digital learning tools and platforms in formal and informal learning settings has provided broad access to large amounts of learner data, the analysis of which has been aimed at understanding students' learning processes, improving learning outcomes, providing learner support as well as teaching. Presently, such data has been largely accessed from discussion forums in online learning management systems and has been further analyzed through the application of social network analysis (SNA). Nevertheless, the results of these analyses have not always been reproducible. Since such learning analytics (LA) methods rely on measurement as a first step of the process, the robustness of selected techniques for measuring collaborative learning activities is critical for the transparency, reproducibility and generalizability of the results. This paper presents findings from a study focusing on the validation of critical centrality measures frequently used in the fields of LA and SNA research. We examined how different network configurations (i.e., multigraph, weighted, and simplified) influence the reproducibility and robustness of centrality measures as indicators of student learning in CSCL settings. In particular, this research aims to contribute to the provision of robust and valid methods for measuring and better understanding of the participation and social dimensions of collaborative learning. The study was conducted based on a dataset of 12 university courses. The results show that multigraph configuration produces the most consistent and robust centrality measures. The findings also show that degree centralities calculated with the multigraph methods are reliable indicators for students' participatory efforts as well as a consistent predictor of their performance. Similarly, Eigenvector centrality was the most consistent centrality that reliably represented social dimension, regardless of the network configuration. This study offers guidance on the appropriate network representation as well as sound recommendations about how to reliably select the appropriate metrics for each dimension.
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| 33. |
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| 34. |
- Saqr, Mohammed, et al.
(författare)
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High resolution temporal network analysis to understand and improve collaborative learning
- 2020
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Ingår i: LAK '20. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450377126 ; , s. 314-319
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Konferensbidrag (refereegranskat)abstract
- There has been significant efforts in studying collaborative and social learning using aggregate networks. Such efforts have demonstrated the worth of the approach by providing insights about the interactions, student and teacher roles, and predictability of performance. However, using an aggregated network discounts the fine resolution of temporal interactions. By doing so, we might overlook the regularities/irregularities of students' interactions, the process of learning regulation, and how and when different actors influence each other. Thus, compressing a complex temporal process such as learning may be oversimplifying and reductionist. Through a temporal network analysis of 54 students interactions (in total 3134 interactions) in an online medical education course, this study contributes with a methodological approach to building, visualizing and quantitatively analyzing temporal networks, that could help educational practitioners understand important temporal aspects of collaborative learning that might need attention and action. Furthermore, the analysis conducted emphasize the importance of considering the time characteristics of the data that should be used when attempting to, for instance, implement early predictions of performance and early detection of students and groups that need support and attention.
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| 35. |
- Saqr, Mohammed, et al.
(författare)
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How learning analytics can early predict under-achieving students in a blended medical education course
- 2017
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Ingår i: Medical teacher. - 0142-159X .- 1466-187X. ; 39:7, s. 757-767
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 36. |
- Saqr, Mohammed, et al.
(författare)
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How networking and social capital influence performance : the role of long-term ties
- 2021
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Ingår i: Networks in the Global World V: Proceedings of NetGloW 2020. - Cham : Springer. - 9783030648770 - 9783030648763 ; , s. 335-346
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Konferensbidrag (refereegranskat)abstract
- Recently, students have become networked in many ways, and evidence is mounting that networking plays a significant role in how students learn, interact, and transfer information. Relationships could translate to opportunities; resources and support that help achieve the pursued goals and objectives. Although students exist, interact, and play different roles within social and information networks, networks have not received the due attention. This research aimed to study medical student’s friendship- and information exchange networks as well as assess the correlation between social capital and network position variables and the cumulative Grade Point Average (GPA) which is the average grade obtained over all the years. The relationships considered in our study are the long-term face-to-face and online ties that developed over the full duration of the study in the medical college. More specifically, we have studied face-to-face and information networks. Analysis of student’s networks included a combination of visual and social network analysis. The correlation with the performance was performed using resampling permutation correlation coefficient, linear regression, and 10-fold cross-validation of binary logistic regression. The results of correlation and linear regression tests demonstrated that student’s social capital was correlated with performance. The most significant factors were the power of close friends regarding connectedness and achievement scores. These findings were evident in the close friends’ network and the information network. The results of this study highlight the importance of social capital and networking ties in medical schools and the need to consider peer dynamics in class assignment and support services
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| 37. |
- Saqr, Mohammed, et al.
(författare)
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How social network analysis can be used to monitor online collaborative learning and guide an informed intervention
- 2018
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 13:3
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Tidskriftsartikel (refereegranskat)abstract
- To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students' interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education.
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| 38. |
- Saqr, Mohammed, et al.
(författare)
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How the study of online collaborative learning can guide teachers and predict students' performance in a medical course
- 2018
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Ingår i: BMC Medical Education. - : Springer Science and Business Media LLC. - 1472-6920. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- Background: Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance. Methods: Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students' performance was calculated, and automatic linear regression was used to predict students' performance. Results: By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user's position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student's position and role in information relay in online case discussions, combined with the strength of that student's network (social capital), can be used as predictors of performance in relevant settings. Conclusion: By using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students' and teachers' interactions that can be valuable in guiding teachers, improve students' engagement, and contribute to learning analytics insights.
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| 39. |
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| 40. |
- Saqr, Mohammed, et al.
(författare)
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Learning and Social Networks–Similarities, Differences and Impact
- 2020
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Ingår i: Proceedings - IEEE 20th International Conference on Advanced Learning Technologies, ICALT 2020July 2020. ; , s. 135-139
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Konferensbidrag (refereegranskat)abstract
- Previous work in learning analytics have been fruitful in shedding lights on collaborative learning environments, such work has provided insights and recommendations that helped improve the collaborative process in computer-mediated learning environments. Given the importance of social interactions and their influence on learning (e.g., in determining academic growth, perseverance in the course and persistence). In this study, we look at both learning and social networks, what factors they share, how they impact or influence learning, and what influences the formation of these networks. Our results show similarities and differences between both networks such as: interactions in the social network predict those in the learning network, however, only centrality measures in the learning network correlate with performance, probably due to the selective nature of replies and interactions in the learning network.
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| 41. |
- Saqr, Mohammed, et al.
(författare)
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Multimodal temporal network analysis to improve learner support and teaching
- 2020
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS. ; , s. 30-33
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Konferensbidrag (refereegranskat)abstract
- A learning process involves interactions between learners, teachers, machines and formal and/or informal learning environments. These interactions are relational, interdependent and temporal. The emergence of rich multimodal learner data suggests the development of methods that can capture time-stamped data from multiple sources (e.g., heart rate data and eye tracking data), thus allowing researchers to examine learning as a continuous process rather than a static one. This leads us to propose a new methodological approach, the Multimodal Temporal Network Analysis to: i) measure temporal learner data deriving from the relevant interactions and ii) ultimately support learners and their teachers in learning and/or teaching activities.
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| 42. |
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| 43. |
- Saqr, Mohammed, et al.
(författare)
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People, Ideas, Milestones : A Scientometric Study of Computational Thinking
- 2021
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Ingår i: ACM Transactions on Computing Education. - : Association for Computing Machinery (ACM). - 1946-6226. ; 21:3
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Tidskriftsartikel (refereegranskat)abstract
- The momentum around computational thinking (CT) has kindled a rising wave of research initiatives andscholarly contributions seeking to capitalize on the opportunities that CT could bring. A number of literaturereviews have showed a vibrant community of practitioners and a growing number of publications. However,the history and evolution of the emerging research topic, the milestone publications that have shaped itsdirections, and the timeline of the important developments may be better told through a quantitative, scientometric narrative. This article presents a bibliometric analysis of the drivers of the CT topic, as well as itsmain themes of research, international collaborations, influential authors, and seminal publications, and howauthors and publications have influenced one another. The metadata of 1,874 documents were retrieved fromthe Scopus database using the keyword “computational thinking.” The results show that CT research has been US-centric from the start, and continues to be dominated by US researchers both in volume and impact. International collaboration is relatively low, but clusters of joint research are found between, for example, anumber of Nordic countries, lusophone- and hispanophone countries, and central European countries. The results show that CT features the computing’s traditional tripartite disciplinary structure (design, modeling, and theory), a distinct emphasis on programming, and a strong pedagogical and educational backdrop including constructionism, self-efficacy, motivation, and teacher training.
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| 44. |
- Saqr, Mohammed, et al.
(författare)
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Robustness and rich clubs in collaborative learning groups : A learning analytics study using network science
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Productive and effective collaborative learning is rarely a spontaneous phenomenon but rather the result of meeting a set of conditions, orchestrating and scaffolding productive interactions. Several studies have demonstrated that conflicts can have detrimental effects on student collaboration. Through the application of network science, and social network analysis in particular, this learning analytics study investigates the concept of group robustness; that is, the capacity of collaborative groups to remain functional despite the withdrawal or absence of group members, and its relation to group performance in the frame of collaborative learning. Data on all student and teacher interactions were collected from two phases of a course in medical education that employed an online learning environment. Visual and mathematical analysis were conducted, simulating the removal of actors and its effect on the group’s robustness and network structure. In addition, the extracted network parameters were used as features in machine learning algorithms to predict student performance. The study contributes findings that demonstrate the use of network science to shed light on essential elements of collaborative learning and demonstrates how the concept and measures of group robustness can increase understanding of the conditions of productive collaborative learning. It also contributes to understanding how certain interaction patterns can help to promote the sustainability or robustness of groups, while other interaction patterns can make the group more vulnerable to withdrawal and dysfunction. The finding also indicate that teachers can be a driving factor behind the formation of rich clubs of well-connected few and less connected many in some cases and can contribute to a more collaborative and sustainable process where every student is included.
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| 45. |
- Saqr, Mohammed, et al.
(författare)
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Tear down the walls : Disseminating open access research for a global impact.
- 2020
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Ingår i: International journal of health sciences. - 1658-3639. ; 14:5, s. 43-49
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Publications are the cornerstone of the dissemination of scientific innovation and scholarly work, but published works are mostly behind paywalls. Therefore, many researchers and institutions are searching for alternative models for disseminating scholarly work that bypasses the current structure of paywalls. This study aimed to determine whether a self-published open access (OA) journal, the International Journal of Health Sciences (IJHS), has been able to reach a global audience in terms of authorship, readership, and impact using the OA model.Methods: All IJHS articles were retrieved and analyzed using scientometric methods. Using the keywords from abstracts and titles, unsupervised clustering was performed to map research trends. Network analysis was used to chart the network of collaboration. The analysis of articles' metadata and the visualizations was performed using R programming language.Results: Using Google Scholar as a source, the general statistics of IJHS from inception to 2019 showed that the average citation per article was 11.29, and the impact factor of the journal was 2.28. The results demonstrate the obvious local and global impact of a locally published journal that allows unrestricted OA and uses an open source publishing platform. The journal's success at attracting diverse topics, authors, and readers is a testament to the power of the OA model.Conclusions: Open source is feasible and rewarding and enables a global reach for research from under-represented regions. Local journals can help the Global South disseminate their scholarly work, which is frequently ignored by commercial and established publications.
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| 46. |
- Saqr, Mohammed, et al.
(författare)
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Temporality matters : A learning analytics study of the patterns of interactions and its relation to performance
- 2018
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Ingår i: EDULEARN18. - : The International Academy of Technology, Education and Development. - 9788409027095 ; , s. 5386-5393
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Konferensbidrag (refereegranskat)abstract
- Although temporality is embodied in instructional design, implicitly present in several learning theories and central to the self-regulation of learning and awarding of credits, it has not received the due attention in the field education. This learning analytics study included four higher education courses in dental education over a full year duration. Temporality in terms of when students engage in learning was studied on daily, weekly, course, and year basis. The patterns of low and high achiever groups in each period were visually plotted and compared. Correlation with the performance was evaluated using the non-parametric Spearman correlation test using re-sampling permutation technique. The findings of this study highlight some important points; temporality is a defining factor of how students regulate their learning and should be taken into account when designing a possible monitoring system. High achievers were always active early in the year, in the course, and on assignments. Low achievers, on the other hand, tend to be significantly more active close to examination times. Using only temporality predictors, we were able to predict high achievers with 100% precision and low achievers with 82.3% to 93.3% class precision. Since early participation was the predictor, it means that an early alert indicator can be achieved that enables timely intervention.
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| 47. |
- Saqr, Mohammed, 1975-, et al.
(författare)
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The Dire Cost of Early Disengagement : A Four-Year Learning Analytics Study over a Full Program
- 2021
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Ingår i: EC-TEL 2021: Technology-Enhanced Learning for a Free, Safe, and Sustainable World. - Cham : Springer Nature. ; , s. 122-136
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Konferensbidrag (refereegranskat)abstract
- Research on online engagement is abundant. However, most of the available studies have focused on a single course. Therefore, little is known about how students’ online engagement evolves over time. Previous research in face-to-face settings has shown that early disengagement has negative consequences on students’ academic achievement and graduation rates. This study examines the longitudinal trajectory of students’ online engagement throughout a complete college degree. The study followed 99 students over 4 years of college education including all their course data (15 courses and 1383 course enrollments). Students’ engagement states for each course enrollment were identified through Latent Class Analysis (LCA). Students who were not engaged at least one course in the first term was labeled as “Early Disengagement”, whereas the remaining students were labeled as “Early Engagement”. The two groups of students were analyzed using sequence pattern mining methods. The stability (persistence of the engagement state), transition (ascending to a higher engagement state or descending to a lower state), and typology of each group trajectory of engagement are described in this study. Our results show that early disengagement is linked to higher rates of dropout, lower scores, and lower graduation rates whereas early engagement is relatively stable. Our findings indicate that it is critical to proactively address early disengagement during a program, watch the alarming signs such as presence of disengagement during the first courses, declining engagement along the program, or history of frequent disengagement states.
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| 48. |
- Saqr, Mohammed, et al.
(författare)
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The longitudinal association between engagement and achievement varies by time, students' profiles, and achievement state : A full program study
- 2023
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Ingår i: Computers and education. - : Elsevier BV. - 0360-1315 .- 1873-782X. ; 199
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Tidskriftsartikel (refereegranskat)abstract
- There is a paucity of longitudinal studies in online learning across courses or throughout pro-grams. Our study intends to add to this emerging body of research by analyzing the longitudinal trajectories of interaction between student engagement and achievement over a full four-year program. We use learning analytics and life-course methods to study how achievement and engagement are intertwined and how such relationship evolves over a full program for 106 students. Our findings have indicated that the association between engagement and achievement varies between students and progresses differently between such groups over time. Our results showed that online engagement at any single time-point is not a consistent indicator for high achievement. It takes more than a single point of time to reliably forecast high achievement throughout the program. Longitudinal high grades, or longitudinal high levels of engagement (either separately or combined) were indicators of a stable academic trajectory in which students remained engaged -at least on average- and had a higher level of achievement. On the other hand, disengagement at any time point was consistently associated with lower achievement among low-engaged students. Improving to a higher level of engagement was associated with -at least- acceptable achievement levels and rare dropouts. Lack of improvement or "catching up" may be a more ominous sign that should be proactively addressed.
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| 49. |
- Saqr, Mohammed, et al.
(författare)
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The relational, co-temporal, contemporaneous, and longitudinal dynamics of self-regulation for academic writing
- 2021
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Ingår i: RESEARCH AND PRACTICE IN TECHNOLOGY ENHANCED LEARNING. - : Springer Nature. - 1793-7078. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Writing in an academic context often requires students in higher education to acquire a new set of skills while familiarising themselves with the goals, objectives and requirements of the new learning environment. Students' ability to continuously self-regulate their writing process, therefore, is seen as a determining factor in their learning success. In order to study students' self-regulated learning (SRL) behaviour, research has increasingly been tapping into learning analytics (LA) methods in recent years, making use of multimodal trace data that can be obtained from students writing and working online. Nevertheless, little is still known about the ways students apply and govern SRL processes for academic writing online, and about how their SRL behaviour might change over time. To provide new perspectives on the use of LA approaches to examine SRL, this study applied a range of methods to investigate what they could tell us about the evolution of SRL tactics and strategies on a relational, co-temporal, contemporaneous and longitudinal level. The data originates from a case study in which a private Facebook group served as an online collaboration space in a first-year academic writing course for foreign language majors of English. The findings show that learners use a range of SRL tactics to manage their writing tasks and that different tactic can take up key positions in this process over time. Several shifts could be observed in students' behaviour, from mainly addressing content-specific topics to more form-specific and social ones. Our results have also demonstrated that different methods can be used to study the relational, co-temporal, contemporaneous, and longitudinal dynamics of self-regulation in this regard, demonstrating the wealth of insights LA methods can bring to the table.
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| 50. |
- Saqr, Mohammed, et al.
(författare)
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The role of social network analysis as a learning analytics tool in online problem based learning
- 2019
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Ingår i: BMC Medical Education. - : Springer Science and Business Media LLC. - 1472-6920. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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