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- 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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- 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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- 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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| 6. |
- 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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| 8. |
- 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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| 9. |
- 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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| 10. |
- Saqr, Mohammed, et al.
(författare)
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Time to focus on the temporal dimension of learning : A learning analytics study of the temporal patterns of students’ interactions and self-regulation
- 2019
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Ingår i: International Journal of Technology Enhanced Learning. - 1753-5255 .- 1753-5263. ; 11:4, s. 398-412
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Tidskriftsartikel (refereegranskat)abstract
- In this learning analytics study, we attempt to understand the role of temporality measures for the prediction of academic performance. The study included four online courses over a full-year duration. Temporality was studied on daily, weekly, course-wise and year-wise. Visualising the activities has highlighted certain patterns. On the week level, early participation was a consistent predictor of high achievement. This finding was consistent from course to course and during most periods of the year. On course level, high achievers were also likely to participate early and consistently. With a focus on temporal measures, we were able to predict high achievers with reasonable accuracy in each course. These findings highlight the idea that temporality dimension is a significant source of information about learning patterns and has the potential to inform educators about students’ activities and to improve the accuracy and reproducibility of predicting students’ performance.
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| 11. |
- Saqr, Mohammed, et al.
(författare)
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Towards group-aware learning analytics : using social network analysis and machine learning to monitor and predict performance in collaborative learning
- 2019
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Ingår i: INTED2019. - : The International Academy of Technology, Education and Development. - 9788409086191 ; , s. 7652-7659
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Konferensbidrag (refereegranskat)abstract
- We know that employing collaborative learning strategies does not lead to productive collaborative learning per se. In fact, some groups are dysfunctional and might have a detrimental influence on group members. This issue has methodologically been studied through self-reported surveys, transcripts coding, and observational methods. Although these methods are informative, they are also time intensive, exhausting and not practical to be applied in real practice. Social network analysis (SNA) and learning analytics, on the other hand, open the door for using automatic and non-intrusive methods that can help us monitor group interactions. Here we study if SNA combined with machine learning techniques can be employed in order to better understand and predict how different type and quantities of collaborative interactions in online environments affect individual and group performance. More specifically, we study the correlation between group interaction parameters as measured by SNA and groups and individual’s performance. Using interaction parameters and machine learning methods, we identify the indicators that best predict groups that will perform and gain and groups that will not, as well as individuals who gain in performance and those who do not. The article provides support for the idea that learning analytics can help automatically monitor group performance and offer an opportunity for educators and learners to support productive collaborative learning.
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