Open University Learning Analytics dataset

Kužílek, Jakub; Hlosta, Martin; Zdráhal, Zdeněk

doi:10.1038/sdata.2017.171

Cited by 287 publications

(209 citation statements)

References 3 publications

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“…The experiments were conducted on four level-1 university courses with 1200 to 2500 students on the publicly available OULAD -Open University Learning Analytics Dataset [17].…”

Section: Methodsmentioning

confidence: 99%

Ouroboros

Hlosta

Zdráhal

Zendulka

2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

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This paper focuses on the problem of identifying students, who are at risk of failing their course. The presented method proposes a solution in the absence of data from previous courses, which are usually used for training machine learning models. This situation typically occurs in new courses. We present the concept of a "self-learner" that builds the machine learning models from the data generated during the current course. The approach utilises information about already submitted assessments, which introduces the problem of imbalanced data for training and testing the classification models.There are three main contributions of this paper: (1) the concept of training the models for identifying at-risk students using data from the current course, (2) specifying the problem as a classification task, and (3) tackling the challenge of imbalanced data, which appears both in training and testing data.The results show the comparison with the traditional approach of learning the models from the legacy course data, validating the proposed concept.

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“…The experiments were conducted on four level-1 university courses with 1200 to 2500 students on the publicly available OULAD -Open University Learning Analytics Dataset [17].…”

Section: Methodsmentioning

confidence: 99%

Ouroboros

Hlosta

Zdráhal

Zendulka

2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

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“…Students' interactions with the educational materials are recorded and stored in university data warehouse. The dataset contains information about 22 courses delivered by the OU, 32,593 students, their assessment results, and logs of their interactions online is represented by daily summaries of student clicks (10,655,280 entries) [13].…”

Section: A Time Series Data Constructionmentioning

confidence: 99%

A Time Series Classification Method for Behaviour-Based Dropout Prediction

Liu¹,

Wang

Benachour

et al. 2018

2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT)

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Students' dropout rate is a key metric in online distance learning courses such as MOOCs. We propose a timeseries classification method to construct data based on students' behavior and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.

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“…The OULAD [8] contains information about 32,593 students visiting 22 Open University courses in years 2013 and 2014. The Open University is largest distance learning institution in the United Kingdom with more than 170,000 students.…”

Section: Datamentioning

confidence: 99%

“…Usually, course VLE provides a plan of activities for the whole course and it is recommended for students to follow it. For more details see the original paper [8].…”

Section: Datamentioning

confidence: 99%

“…Our research focused on the exploration of student behaviour using VLE logs in order to uncover behaviour leading to withdrawal or passive withdrawal of the student. For that purpose, we employed Markov chain modelling [7] on behavioural data available in Open University Learning Analytics dataset (OULAD) [8], which contains the data from a Moodle-like system used at the Open University 2 . Furthermore, the previously used approach [4] has been simplified and the state space of student activities was reduced to 7 possible states, which will be further discussed in section 3…”

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confidence: 99%

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Student Drop-out Modelling Using Virtual Learning Environment Behaviour Data

Kužílek

Vaclavek

Fuglík

et al. 2018

Lifelong Technology-Enhanced Learning

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With the rapid advancement of Virtual Learning Environments (VLE) in higher education, the amount of available student data grows. Universities collect the information about students, their demographics, their study results and their behaviour in the online environment. By applying modelling and predictive analysis methods it is possible to predict student outcome or detect bottlenecks in course design. Our work aims at statistical simulation of student behaviour in the VLE in order to identify behavioural patterns leading to drop-out or passive withdrawal i.e. the state when a student is not studying, but he has not actively withdrawn from studies. For that purpose, the method called Markov chain modelling has been used. Recorded student activities in VLE (VLE logs) has been used for constructing of probabilistic representation that students will perform some activity in the next week based on their activities in the current week. The result is an instance of the family of absorbing Markov chains, which can be analysed using the property called time to absorption. The preliminary results show that interesting patterns in student VLE behaviour can be uncovered, especially when combined with the information about submission of the first assessment. Our analysis has been performed using Open University Learning Analytics dataset (OULAD) and research notes are available online 1 .

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Open University Learning Analytics dataset

Cited by 287 publications

References 3 publications

Ouroboros

Ouroboros

A Time Series Classification Method for Behaviour-Based Dropout Prediction

Student Drop-out Modelling Using Virtual Learning Environment Behaviour Data

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