Learning analytics techniques and visualisation with textual data for determining causes of academic failure

Nkhoma, Clara; Dang-Pham, Duy; Hoang, Ai Phuong; Nkhoma, Mathews; Le-Hoai, Tram; Thomas, Susan

doi:10.1080/0144929x.2019.1617349

Cited by 10 publications

(8 citation statements)

References 51 publications

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“…In another study, Nkhoma et al (2019) identified the reasons behind underperformance of the students in a university in Southeast Asia. The data were collected from 968 letters written by students who were at risk of failure.…”

Section: Literature Reviewmentioning

confidence: 99%

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Exploring the Reasons Behind Iranian TEFL Graduate Students’ Academic Failure

Alemi

Rezanejad

Marefat

2021

Profile: Issues Teach. Prof. Dev.

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This cross-sectional study explored the reasons behind academic failure among Iranian students of teaching of English as a foreign language. Interviews were used to collect data from 56 graduate students (19 men and 37 women) and three officials of the university. Results indicated that four main factors led to the academic failure of the students, namely, (1) the student, (2) the professor, (3) the university, and (4) the source materials. Moreover, the results of chi-square tests indicated that no significant relationship existed between the gender and age of the students and their academic failure. Finally, a number of guidelines to prevent academic failure in this context are presented.

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Section: Literature Reviewmentioning

confidence: 99%

“…To stress the significance, Ajjawi et al (2019) call it a "world-wide problem." In fact, academia plays a very important role in the success or failure of the students (Nkhoma et al, 2019). Accordingly, identification of academic failure in the early stages of education has been brought into the limelight (Sarra et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring the Reasons Behind Iranian TEFL Graduate Students’ Academic Failure

Alemi

Rezanejad

Marefat

2021

Profile: Issues Teach. Prof. Dev.

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“…The networks discussed in this special section depict networks of clickstream data generated by learners in a large online course, and networks of phases in self-regulated learning, respectively (Zhang et al, 2022;Malmberg et al, 2022). It is also common to find research making use of word cooccurrence networks to conduct a semantic analysis of content generated by learners (Nkhoma et al, 2020). All these studies provide new insight into different aspects of the learning process; for instance, learners' distribution of attention (Zhang et al, 2022, this issue) and their metacognitive strategies (Malmberg et al, 2022, this issue), that would not have been possible without the network science approach.…”

Section: Introductionmentioning

confidence: 99%

Investigating Cognitive Network Models of Learners’ Knowledge Representations

Siew

2022

Learning Analytics

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This commentary discusses how research approaches from Cognitive Network Science can be of relevance to research in the field of Learning Analytics, with a focus on modelling the knowledge representations of learners and students as a network of interrelated concepts. After providing a brief overview of research in Cognitive Network Science, I suggest that a focus on the cognitive processes that occur in the knowledge network, as well as the mechanisms that give rise to changes in the structure of knowledge networks, can lead to potentially informative insights into how learners navigate their knowledge representations to retrieve information and how the knowledge representations of learners develop and grow over the course of their educational careers. Learning Analytics can leverage these insights to design adaptive learning or online learning platforms that optimize learning, and inform pedagogical practice and assessment design that support the development of effective and robust knowledge structures.

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“…In a dynamic educational environment, maintaining financial stability, solvency, investment attractiveness and ensuring the growth of market value is a challenge for any business entity, including universities (Looser & Mohr, 2020). The variability of the external environment of the functioning of economic entities and their internal features put forward new requirements for the existing management system, and necessitate its improvement and adaptation to new needs (Nkhoma et al, 2020). In the process of making and implementing management decisions, it is likely impossible to achieve target indicators in the areas of activity, which is due to the influence of risk factors on the university (Fan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Formation of a Programmatic Management Mechanism for Risk Reduction in Educational Activity

Askhadullina

Vishnyakova

Aminova

et al. 2021

Journal of Education

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The modern educational environment is characterized by an increase in the influence of factors of uncertainty and instability. Based on a comparative analysis of the concepts of control, their improved typology has been formed. To form the model of the risk control system, the authors have chosen the concepts of control, focused on the management information system, the management system with an emphasis on management functions and coordination of the management process. The definition of the economic category “risk control” was formulated. The theoretical foundations of the formation of the risk control system of educational institutions have been improved.

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Learning analytics techniques and visualisation with textual data for determining causes of academic failure

Cited by 10 publications

References 51 publications

Exploring the Reasons Behind Iranian TEFL Graduate Students’ Academic Failure

Exploring the Reasons Behind Iranian TEFL Graduate Students’ Academic Failure

Investigating Cognitive Network Models of Learners’ Knowledge Representations

Formation of a Programmatic Management Mechanism for Risk Reduction in Educational Activity

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