Learning Analytics: A Survey

Keshavamurthy, Usha; Guruprasad, H S

doi:10.14445/22312803/ijctt-v18p155

Cited by 12 publications

(10 citation statements)

References 23 publications

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“…Analysis of such data -learning analytics -allows for monitoring learning consistency, student progress and assignment performance. Learning analytics is based on analyzing big data on learning behaviors in MOOCs [Usha Keshavamurthy, Guruprasad 2014]. It can provide a lot of information on the causes of learner success and failure and allows for predicting future learning behaviors.…”

Section: Factors Of Online Learning Effectivenessmentioning

confidence: 99%

Learning Analytics in Massive Open Online Courses as a Tool for Predicting Learner Performance

Bystrova¹,

Larionova²,

Синицын³

et al. 2018

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Быстрова Татьяна Юрьевна — доктор философских наук, профессор Уральского гуманитарного института. E-mail: taby27@yandex.ruЛарионова Виола Анатольевна — кандидат физико-математических наук, доцент, заместитель проректора, заведующий кафедрой Высшей школы экономики и менеджмента. E-mail: v.a.larionova@urfu.ruСиницын Евгений Валентинович — доктор физико-математических наук, профессор Высшей школы экономики и менеджмента. E-mail: e. v.sinitcyn@urfu.ru.Толмачев Александр Владимирович — старший преподаватель Высшей школы экономики и менеджмента. E-mail: avtolmachev@urfu.ruФГАОУ ВО «Уральский федеральный университет имени первого Президента России Б. Н. Ельцина». Адрес: 620002, Екатеринбург, ул. Мира, 19.Авторы предлагают использовать данные учебной аналитики массовых открытых онлайн-курсов в качестве основы для прогнозирования успешности обучающихся. Такое прогнозирование актуально в условиях запроса на адаптивное обучение, к которому сегодня приходит высшая школа. Междисциплинарная методология статьи позволяет интерпретировать эмпирические количественные данные о выполнении отдельных видов заданий онлайн-курса для составления прогноза успеваемости обучающихся, с одной стороны, и корректировки качества онлайн-курсов— с другой. Результаты учебной аналитики дают возможность максимально учитывать особенности работы обучающихся с информацией и степень предварительной подготовленности к курсу. Приводятся результаты применения предлагаемого алгоритма учебной аналитики для анализа успешности обучающихся на конкретных онлайн-курсах, созданных в Уральском федеральном университете и размещенных на Национальной платформе открытого образования.

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Section: Factors Of Online Learning Effectivenessmentioning

confidence: 99%

Learning Analytics in Massive Open Online Courses as a Tool for Predicting Learner Performance

Bystrova¹,

Larionova²,

Синицын³

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Historically at the intersection of machine learning, statistics, human–computer interaction, and learning science, they were already erected as a full discipline in 2013 (Siemens, 2013). The general objective is to exploit the large amount of learning traces left by students through their interactions with online educational services (intelligent tutoring systems, learning management systems, MOOCs, and so on) to improve the quality of the teaching (Keshavamurthy & Guruprasad, 2015). These systems have rapidly gained popularity and are now used in nursery school and elementary school (Medeiros Machado et al, 2020; Ebner & Schön, 2013; Martinez & LaLonde, 2020; Triana et al, 2015), high school (Jones, 2019), university (Leitner et al, 2017), and even for lifelong learning in companies (Domenjoz, 2017).…”

Section: Introductionmentioning

confidence: 99%

Modelling children's inhibitory skills using learning data from an educational app

Machado

Bonnin

Castagnos

et al. 2023

Computer Assisted Learning

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Background Early literacy and numeracy skills are developed during early childhood. Among the many factors that influence the development of such skills, the literature shows that the executive functions, especially the response inhibition (RI)—that is the capability to block out or to tune out what can be considered irrelevant information or action to the learning task—is one of the most essential functions. There are specific tests used to appraise these children's inhibition skills, but these tests are generally time‐consuming, and demand specialized human resources. Objectives We present a computational approach to model children's RI behaviour through the analysis of educational traces left in an educational app. This modelling allows the automatic and instant identification of the RI level of children without the need of a human‐conducted test. Methods Our modelling is based on two definitions of RI found in the literature, from which we derived a mathematical formalism of three variables we used to query the traces dataset and isolate the RI behaviour of each student from the learning traces generated in the app. The sample population is composed of children from diverse socioeconomic backgrounds. The model is then assessed by comparing it to a traditional human‐conducted RI test suitable for kindergarten children, the Head‐Toes‐Knees‐Shoulder (HTKS) task. Results and conclusions The results show that our RI model can explain an important part of the HTKS variance (up to 0.45 according to the adjusted R2) when taking the HTKS results as a dependent variable for a multiple regression model. In practice, our model can be integrated in a learning app and become a powerful tool for instant preliminary identification of dysfunctional RI behaviour, especially in the early stages of children's education. Once students are identified by our model as having a dysfunctional RI behaviour, teachers can rapidly act to help them. Besides, the proposed model requires only very simple data to work, which means it can be easily integrated into different learning apps.

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“…One important task in learning analytics (LA) [1] and educational data mining (EDM) [2] research is student dropout prediction (SDP). SDP is an important educational problem because of the high dropout rate, mainly from e-learning environments.…”

Section: Introductionmentioning

confidence: 99%

“…In this task, there is a set of already labeled training data (students who have already completed the course). Therefore, it is known whether they dropped out (labeled class 1) or successfully completed the course (labeled class 0). The task aims to make the earliest possible prediction of the label or class for new students taking a new course.…”

Section: Introductionmentioning

confidence: 99%

A Case-Study Comparison of Machine Learning Approaches for Predicting Student’s Dropout from Multiple Online Educational Entities

Porras,

Lara,

Romero

et al. 2023

Algorithms

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Predicting student dropout is a crucial task in online education. Traditionally, each educational entity (institution, university, faculty, department, etc.) creates and uses its own prediction model starting from its own data. However, that approach is not always feasible or advisable and may depend on the availability of data, local infrastructure, and resources. In those cases, there are various machine learning approaches for sharing data and/or models between educational entities, using a classical centralized machine learning approach or other more advanced approaches such as transfer learning or federated learning. In this paper, we used data from three different LMS Moodle servers representing homogeneous different-sized educational entities. We tested the performance of the different machine learning approaches for the problem of predicting student dropout with multiple educational entities involved. We used a deep learning algorithm as a predictive classifier method. Our preliminary findings provide useful information on the benefits and drawbacks of each approach, as well as suggestions for enhancing performance when there are multiple institutions. In our case, repurposed transfer learning, stacked transfer learning, and centralized approaches produced similar or better results than the locally trained models for most of the entities.

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Learning Analytics: A Survey

Cited by 12 publications

References 23 publications

Learning Analytics in Massive Open Online Courses as a Tool for Predicting Learner Performance

Learning Analytics in Massive Open Online Courses as a Tool for Predicting Learner Performance

Modelling children's inhibitory skills using learning data from an educational app

A Case-Study Comparison of Machine Learning Approaches for Predicting Student’s Dropout from Multiple Online Educational Entities

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