Analysing content and patterns of interaction for improving the learning design of networked learning environments

Haya, Pablo A.; Daems, Oliver; Malzahn, Nils; Castellanos, Jorge; Hoppe, H. Ulrich

doi:10.1111/bjet.12264

Cited by 46 publications

(50 citation statements)

References 30 publications

(29 reference statements)

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“…Haya, Daems, Malzahn, Castellanos and Hoppe () consider the complex pedagogical framework of the JuxtaLearn project, which involves student production, team work and critical discussions between teams. Such situations, while very rich from a learning science perspective, are often hard to manage from a pragmatic point of view.…”

Section: The Papers In This Special Issuementioning

confidence: 99%

Editorial: Learning design, teacher inquiry into student learning and learning analytics: A call for action

Mor¹,

Ferguson²,

Wasson³

2015

Brit J Educational Tech

170

139

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This special issue deals with three areas. Learning design is the practice of devising effective learning experiences aimed at achieving defined educational objectives in a given context. Teacher inquiry is an approach to professional development and capacity building in education in which teachers study their own and their peers' practice. Learning analytics use data about learners and their contexts to understand and optimise learning and the environments in which it takes place. Typically, these three-design, inquiry and analytics-are seen as separate areas of practice and research. In this issue, we show that the three can work together to form a virtuous circle. Within this circle, learning analytics offers a powerful set of tools for teacher inquiry, feeding back into improved learning design. Learning design provides a semantic structure for analytics, whereas teacher inquiry defines meaningful questions to analyse.

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Section: The Papers In This Special Issuementioning

confidence: 99%

Editorial: Learning design, teacher inquiry into student learning and learning analytics: A call for action

Mor¹,

Ferguson²,

Wasson³

2015

Brit J Educational Tech

170

139

View full text Add to dashboard Cite

show abstract

“…A wide body of literature suggests that predictive learning analytics (PLA) can help teachers and institutions in general, to overcome such challenges, by identifying which groups of students might need extra support to reach desired learning outcomes (Conijn, Snijders, Kleingeld, & Matzat, ; Haya et al , ; Herodotou et al , ; Kovanović et al , ; Scheffel et al , ). For example, Scheffel et al () found that visualizing the relative performance of 172 students using the PLA supported the learning processes over time.…”

Section: Introductionmentioning

confidence: 99%

“…British Journal of Educational Technology Vol 50 No 6 2019& Matzat, 2017Haya et al, 2015;Herodotou et al, 2017;Kovanović et al, 2015;Scheffel et al, 2017). For example, Scheffel et al (2017) found that visualizing the relative performance of 172 students using the PLA supported the learning processes over time.…”

mentioning

confidence: 99%

Empowering online teachers through predictive learning analytics

Herodotou¹,

Hlosta²,

Boroowa³

et al. 2019

Brit J Educational Tech

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This study presents an advanced predictive learning analytics system, OU Analyse (OUA), and evidence from its evaluation with online teachers at a distance learning university. OUA is a predictive system that uses machine learning methods for the early identification of students at risk of not submitting (or failing) their next assignment. Teachers have access, via interactive dashboards, to weekly predictions of risk of failing for each of their students. In this study, we examined how the degree of OUA usage by 559 teachers, of which 189 were given access to OUA, related to student learning outcomes of more than 14 000 students in 15 undergraduate courses. Teachers who made “average” use of OUA, that is accessed OUA throughout the life cycle of a course presentation, and in particular between 10% and 40% of the weeks a course was running, and intervened with students flagged as at risk were found to benefit their students the most; after controlling for differences in academic performance, these students were found to have significantly better performance than their peers in the previous year's course presentation during which the same teachers made no use of predictive learning analytics. Predictive learning analytics is an innovative student's support approach in online pedagogy that, as shown in this study, can empower online teachers in effectively monitoring and intervening with their students, over and above other approaches, and result in improved learning outcomes. What is already known about this topic Pedagogical and personal support to students is a significant responsibility of online teachers. Student's support is a challenging activity due to the lack of face‐to‐face interactions. Predictive learning analytics (PLA) can identify students at risk of failing their studies. What this paper adds One of the few large‐scale studies is available for examining the impact of analytics on student's performance. Teachers' usage of PLA was significantly related to better learning outcomes. Online teachers had students with better learning outcomes when accessing PLA data rather than when they had no access. Implications for practice and/or policy PLA can empower online teachers and complement the teaching practice. PLA can help in the identification and proactive intervention of students at risk of failing their studies. Actions should be taken to motivate and engage online teachers with PLA.

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“…Algunos estudios se han dirigido a analizar cómo una analítica del aprendizaje puede proporcionar a los profesores una visión profunda del proceso de aprendizaje (Fidalgo-Blanco et al, 2015;Haya et al, 2015;Rodríguez-Triana et al, 2015), mientras que otros han puesto el foco en el diseño de algoritmos que automatizan la minería de datos (Romero et al, 2007), o en la elaboración de propuestas visuales para presentar los resultados de los análisis (Mazza y Dimitrova, 2007;Valentín y Burgos, 2015).…”

Section: Discusión Y Conclusionesunclassified

La actividad de los participantes como fuente de información para promover la colaboración. Una analítica del aprendizaje basada en el modelo de Influencia Educativa Distribuida

Coll¹,

Engel²,

Encinas³

2017

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RESUMENEste trabajo presenta la versión inicial de una analítica del aprendizaje inspirada en el modelo de influencia educativa distribuida (IED). La idea central del modelo de IED es que en las situaciones de trabajo y aprendizaje colaborativo todos los participantes son fuentes potenciales de ayuda para los otros participantes. Sobre esta base, se ha llevado a cabo una investigación con un triple objetivo: analizar el impacto de la información proporcionada a los participantes sobre el proceso colaborativo y su evolución, indagar si la información proporcionada tiene un efecto diferencial en función de su naturaleza y contrastar la utilidad de la analítica desarrollada para analizar los procesos de colaboración en línea. Para ello, se han seguido cuatro grupos de estudiantes que participan en una serie de foros en línea en el marco de una asignatura de máster. Los resultados indican que en general la información proporcionada a los participantes sobre su actividad tiene un impacto en el proceso colaborativo. No ha sido posible, en cambio, demostrar que la naturaleza de la información proporcionada tenga un impacto diferencial sobre el proceso de colaboración. Los resultados también han puesto de manifiesto algunas limitaciones de los indicadores en que se concreta la analítica desarrollada. PALABRAS CLAVE: analíticas del aprendizaje, aprendizaje colaborativo, foros en línea, influencia educativa, influencia educativa distribuida.

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Analysing content and patterns of interaction for improving the learning design of networked learning environments

Cited by 46 publications

References 30 publications

Editorial: Learning design, teacher inquiry into student learning and learning analytics: A call for action

Editorial: Learning design, teacher inquiry into student learning and learning analytics: A call for action

Empowering online teachers through predictive learning analytics

La actividad de los participantes como fuente de información para promover la colaboración. Una analítica del aprendizaje basada en el modelo de Influencia Educativa Distribuida

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