Learning Analytics in 21st century education: a review

Laverde, Andrés Chiappe; Rodríguez, Lilian Patricia

doi:10.1590/s0104-40362017002501211

Cited by 19 publications

(19 citation statements)

References 25 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Pareciera ser que los mayores desencuentros acontecen en la manera en que se planifica y gestiona este recurso educativo. Los resultados de los estudios revisados dan cuenta de un choque entre el tiempo objetivo y el tiempo subjetivo, entre el tiempo escolar y el tiempo extraescolar, disidencias que desde nuestro juicio podrían representar una variable importante a considerar para contribuir -en comprensión y solución-respecto de los problemas de equidad y calidad educativa que afectan a Latinoamérica (De la Cruz, 2017; Eslava, 2016) o incluso de los problemas que a nivel educativo se enfrentan internacionalmente (Chiappe y Rodríguez, 2017).…”

Section: Discussionunclassified

Tiempo escolar

González

Cuadra-Martínez

Véliz

et al. 2019

View full text Add to dashboard Cite

El tiempo escolar es clave en la calidad educativa, principalmente en Latinoamérica, por los problemas de calidad y equidad educativa que la afectan. Este trabajo es una revisión teórica de diversos estudios empíricos sobre el impacto de las políticas de extensión del tiempo escolar y cómo este recurso se gestiona, comprende y organiza en las escuelas latinoamericanas. Los resultados se organizan considerando el tiempo escolar con base en: a) su extensión y relación con el mejoramiento educativo; b) el significado que se le otorga; c) su uso como recurso pedagógico; d) y como recurso educativo extraescolar; e) su valor organizacional; y f) las metodologías de los estudios revisados. Finalmente se reflexiona sobre estos resultados y se destaca la necesidad de avanzar en los estudios de tiempo escolar y fortalecer la comprensión de este factor en los centros educativos, a partir de nuevas dimensiones, contextos y agentes educativos.

show abstract

Section: Discussionunclassified

Tiempo escolar

González

Cuadra-Martínez

Véliz

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Learning professionals, through data analysis, could compare convenience samples of their populations to larger educational community samples to validate an instructional methodology variance that needed to be changed to meet the needs of the localized learners. In a literature review, Chiappe and Rodriguez, 2017, found that EDM and the learning analytics involved offer the possibility of educational stakeholders being "better informed and thus make better and timely decisions" (para. 3).…”

Section: Big Data In Educationmentioning

confidence: 99%

“…When discussing the successful integration of technology tools and data analysis to improve learning and instruction, Chiappe and Rodriguez (2017) comment that making better and timely decisions will not be possible if the "skills related to learning analytics continue to be restricted to highly technical profiles" (para. 3).…”

Section: Big Data In Educationmentioning

confidence: 99%

Findability, Accessibility, and Usability of Data Portals in Education

Banker¹

2020

JESP

View full text Add to dashboard Cite

There is a need for stakeholders, at all levels of education, to be able to locate, access, and use the preponderance of data available about schools and students' demographic information and academic performance. The reason for findability, accessibility and usability of the data is for the stakeholders to make data-driven decisions based on trends the data bring to light. However, in many cases, data are located in portals that are cumbersome and difficult to navigate. This article explores the complexity of educational data portals; the wide gap in technology skills of the user of the data; and, interface design challenges.One of the most exciting elements of data analytics in education is the ability it can afford educators to identify and predict trends in many different areas (e.g. academic achievement, student enrollment, student retention, career interests, pedagogical preferences, etc.) for data-driven decisions to improve student learning. The successful use of data analytics starts with educational data mining (EDM). EDM is the process of using computational approaches (statistical algorithms) to analyze different data sets collected by various educational institutions (Romero& Ventura, 2010). In A Roadmap for Educational Technology, 2010, there are seven information technology areas that hold great promise for the future to better help students learn. Those areas are: 1) user modeling; 2) mobile tools; 3) networking tools; 4) serious games; 5) intelligent environments; 6) educational data mining; and, 7) rich interfaces (Computing Community Consortium, 2010, p. 43). As one of the seven information technology areas, EDM is knowledge discovery in databases (Siguenza, Saquicela, Avila-Ordonez, Vandewalle, & Cattrysse, 2015).Knowledge discovery occurs when EDM uses statistical methods that search for new and generalizable relationships rather than testing a hypothesis (Slater, Joksimovic, Kovanovic, Baker & Gasevic, 2017). The new and generalizable relationships can then be used to make decisions that will help learning professionals better design learning for students (Masie, 2013). In other words, the use of EDM can produce knowledge, such as trends and patterns, that was not known before, about the relationship between learning and students from the statistical analysis done on large sets of data. (Siguenza et al., 2015). Due to advances in technology, a large volume, a wide variety, and high speeds of data about students are being generated and collected (Wang, 2017).

show abstract

“…In this sense, the current review represents "a second chapter" that refreshes and complements the LA landscape. Other similar reviews focus on the following targets: Rojas-Castro (2017) analyses the elements that differentiate LA from other disciplines in terms of field, work lines, and maturity to determine how it relates to learning; Liu, Kang, Liu, Zou, and Hodson (2017) review studies using LA in serious games to support teaching and learning, where learner performance and game design strategies are the most common topics; Nunn, Avella, Kanai, and Kebritchi (2016) account for the most used LA methods such as: visual data analysis, social network analysis (SNA), semantic, and educational data mining (EDM) tasks; Chiappe and Rodríguez (2017) uncover a view of educational stakeholders concluding that LA generates valuable knowledge for decision making according to the actors' needs.…”

Section: Prior Reviews Of the Learning Analytics Fieldmentioning

confidence: 99%

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Before the emergence of computer‐based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014–2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state‐of‐the‐art and an acumens vision to inspire future labor. This article is categorized under: Application Areas > Education and Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

show abstract

Learning Analytics in 21st century education: a review

Cited by 19 publications

References 25 publications

Tiempo escolar

Tiempo escolar

Findability, Accessibility, and Usability of Data Portals in Education

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Contact Info

Product

Resources

About