Learning Analytics

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Now showing 1 - 4 of 4
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    A Systematic Review of the Factors that Impact the Prediction of Retention and Dropout in Higher Education
    ( 2023-01-03) Silva, Edmilson Cosme ; Freitas, Sergio ; Soares Ramos, Cristiane ; Muniz De Menezes, Amanda Emilly ; Rodrigues De Araujo, Leticia Karla Soares
    Identifying factors that affect academic dropout and retention is a research area that brings a plurality of opinions and concepts. This article identifies current primary studies to understand the main factors related to dropout and retention. It is quantitative, exploratory, and explanatory research of an applied nature, using the technical procedures of case study and bibliographic research. The systematic review of the literature identifies the factors that impact academic dropout and retention and serves as a basis for a machine learning project. Academic, demographic, and learning factors can predict dropouts and retention. The definition of the factors used and the way of use is essential to obtain good forecasting results. The identified factors were used in the institution.
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    Introduction to the Minitrack on Learning Analytics
    ( 2023-01-03) Islind, Anna Sigridur ; Willermark, Sara ; Óskarsdóttir, Maria ; Deeva, Galina
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    Making the Most of Slides and Lecture Captures for Better Performance: A Learning Analytics Case Study in Higher Education
    ( 2023-01-03) López Flores, Nidia ; Óskarsdóttir, Maria ; Islind, Anna Sigridur
    The provision of educational material in higher education takes place through learning management systems (LMS) and other learning platforms. However, little is known yet about how and when the students access the educational materials provided to perform better. In this paper, we aim to answer the research question: ‘How do the high achievers use the educational material provided to get better grades?’. To answer this question, the data from two educational platforms were merged: a LMS, and a lecture capture platform. We based our analysis on a series of quizzes to understand the differences between high and non high achievers regarding the use of lecture recordings and slides at different moments: (1) before and (2) while solving the quizzes, and (3) after their submission. Our analysis shows significant differences between both groups and highlights the value of considering all the educational platforms instead of limiting the analyses to a single data source.
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    Does Mouse Click Frequency Predict Students' Flow Experience?
    ( 2023-01-03) Muramatsu, Pedro Kenzo ; Oliveira, Wilk ; Hamari, Juho ; Oyibo, Kiemute
    Designing educational systems able to lead students into flow experience is a contemporary challenge, especially given the positive relationship between flow experience and learning. However, an important challenge within the field of learning analytics is evaluating the students' flow experience during the use of educational systems. In general, such evaluation is conducted using invasive methods (e.g., electroencephalogram, and eye trackers) and cannot be massively applied. To face this challenge, following the trend of utilizing behavioral data produced by users to identify their experience when using different types of systems, in our study, we evaluated the applicability of employing one single type of behavior data (i.e., mouse click frequency) as an exclusive metric to model and to predict students' flow experience. By conducting two data-driven studies (N1 = 25 | N2 = 101), we identified that the mouse click frequency on its own is not able to predict the flow experience. Our study contributes to the field of learning analytics confirming that it is not possible to predict students' flow experience only with mouse click frequency and paving the way for new studies that use different behavior data to predict students' flow experience.