Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning

Nouri, Jalal; Larsson, Ken; Saqr, Mohammed

doi:10.1007/978-3-030-29736-7_3

Cited by 17 publications

(18 citation statements)

References 34 publications

(57 reference statements)

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“…However, this student-related factor was less influential than supervisors' average time to complete thesis projects and their ratio of unfinished thesis projects. The finding that students' previous grades are of less importance for thesis completion corroborates previous findings of Tinto [44], Pascarella and Terenzini [45], Astin [46], and Nouri et al [47].…”

Section: Discussionsupporting

confidence: 89%

Bachelor Thesis Analytics: Using Machine Learning to Predict Dropout and Identify Performance Factors

Nouri

Larsson

Saqr

2019

iJAI

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<p class="0abstractCxSpLast">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.</p>

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Section: Discussionsupporting

confidence: 89%

Bachelor Thesis Analytics: Using Machine Learning to Predict Dropout and Identify Performance Factors

Nouri

Larsson

Saqr

2019

iJAI

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“…As Bazrafkan, Yousefy, Amini, and Yamani (2019) state, "Faculty members supervise the students because qualified supervision leads to success on the part of the student, and it has moral, reputational, and financial outcomes for the institution" (p. 1). However, this highly complex process often poses obstacles to both supervisors and students (Minayo, 2019;Roberts & Seaman, 2018;Zaheer & Munir, 2020;Seijas, Val, Antelo, & Rodríguez, 2018;Nouri, Larsson, & Saqr, 2019;Capello, 2020;Alabdulaziz, 2020;Roberts & Seaman, 2017;van Rooij, Fokkens-Bruinsma, & Jansen, 2019). Nouri et al (2019) offer an illustrative view of these difficulties when they state that Not finishing a master thesis is a devastating personal experience for students that costs precious time, loss of money and energy.…”

Section: Thesis Component Learning Outcomementioning

confidence: 99%

“…However, this highly complex process often poses obstacles to both supervisors and students (Minayo, 2019;Roberts & Seaman, 2018;Zaheer & Munir, 2020;Seijas, Val, Antelo, & Rodríguez, 2018;Nouri, Larsson, & Saqr, 2019;Capello, 2020;Alabdulaziz, 2020;Roberts & Seaman, 2017;van Rooij, Fokkens-Bruinsma, & Jansen, 2019). Nouri et al (2019) offer an illustrative view of these difficulties when they state that Not finishing a master thesis is a devastating personal experience for students that costs precious time, loss of money and energy. Non-completion also results in a vast waste of faculty time and institutional resources, and a societal loss of high skilled workers (p. 35).…”

Section: Thesis Component Learning Outcomementioning

confidence: 99%

“…The supervision and the features of both the supervisor and the student, in addition to other factors, have a very relevant weight in the product presented by the student (Strebel, Gürtler, Hulliger, & Lindeque, 2019;van Rooij et al, 2019;Nouri et al, 2019). In the analysis of van Rooij et al (2019) of PhD students' supervision, the following four success predictors stand out:…”

Section: Thesis Component Learning Outcomementioning

confidence: 99%

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The Academic Supervisor of Higher Education Students’ Final Projects: A Gatekeeper of Quality?

Sá¹,

Santos

Serpa

2021

AJIS

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The production and presentation for public discussion of an end-of-programme project, thesis or dissertation are unavoidable elements for the conclusion of academic training in study programmes of various levels, namely bachelor’s, master’s and PhD. Although these end-of-programme projects may take on various forms, as well as different levels of demand, supervision in the production of these projects is one of the critical elements for their success. This article aims to analyse the supervision process, examining the supervision strategies that faculty members may mobilise in their role as academic supervisors. To this end, the authors carried out a bibliographic collection, which made it possible to substantiate the proposals set out throughout this piece of research. The results allow concluding that, to be effective and efficient, supervision strategies need to consider, as a basic assumption, that this is a pedagogical relationship, with asymmetric powers between the supervisor(s) and the student(s), in which it is paramount to acknowledge the different duties of each party. Furthermore, this relationship entails the need for flexibility on the part of the supervisor, taking into account both the characteristics of the supervisor and of the work designed and the development of the entire process, which may motivate and involve several supervision strategies in the same supervision over time/process. This whole dynamic takes place in a context where it seems important to (re)think the final product of the academic supervision process. Received: 30 October 2020 / Accepted: 3 December 2020/ Published: 17 January 2021

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“…These attempts are currently expanding in higher educational settings. For instance, a number of research directions have been explored with a learning analytics approach by a research group at Stockholm University, focusing on aspects such as problem-based learning [60], teacher education [61], collaborative learning [62], self-regulated behavior [63], prediction of student performance in blended learning and in flipped classroom settings [64], and prediction of performance and completion of master-and bachelor thesis [65].…”

Section: Swedenmentioning

confidence: 99%

Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Seven Countries

Nouri

Ebner²,

Saqr

et al. 2019

iJAI

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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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Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning

Cited by 17 publications

References 34 publications

Bachelor Thesis Analytics: Using Machine Learning to Predict Dropout and Identify Performance Factors

Bachelor Thesis Analytics: Using Machine Learning to Predict Dropout and Identify Performance Factors

The Academic Supervisor of Higher Education Students’ Final Projects: A Gatekeeper of Quality?

Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Seven Countries

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