Students’ tool-use within a web enhanced course: Explanatory mechanisms of students’ tool-use pattern

Lust, Griet; Elen, Jan; Clarebout, Geraldine

doi:10.1016/j.chb.2013.03.014

Cited by 61 publications

(88 citation statements)

References 51 publications

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“…Moreover, several studies also report significant associations between learning strategies, derived from trace data, and learning outcomes. For example, Lust et al (2013b) reported that the adopted learning strategy had a significant moderate effect on student academic performance in an undergraduate educational sciences blended learning course. Kovanović et al (2015) showed that learning strategy had a significant and large effect on the quality of knowledge construction evolving from online discussions in a fully online software engineering master's course.…”

Section: Learning Analytics and Learning Strategymentioning

confidence: 99%

“…However, the majority of studies collected only the trace data related to the constructs of these theories. In contrast to this trend, Lust et al (2013b) collected both trace data and self-reports about achievement goal orientations and self-efficacy. Self-reported data were then used to identify associations with strategies identified from trace data, and thus offer interpretations of the identified strategies.…”

Section: Learning Analytics and Learning Strategymentioning

confidence: 99%

“…A common recommendation is that studies involving the use of digital traces and learning analytics methods should start from an existing theory to inform their research questions and operationalize the measurements, and thus establish the use of trace data as valid proxies of constructs under study. This approach is already gaining much traction in the field of learning analytics and can be used as an effective way to study different complex concepts such as motivation (Zhou & Winne, 2012) and study strategy (Lust, Elen, & Clarebout, 2013b). …”

Section: Learning Analytics and Learning Theorymentioning

confidence: 99%

“…According to Azevedo (2015), self-reports, in addition to classroom discourse, are the only proven approach that can be used for the measurement of cognitive, metacognitive, affective, and motivational constructs of student engagement. This provides the rationale for making use of existing self-report instruments to interpret and triangulate findings obtained through the use of trace data (Beheshitha, Hatala, Gašević, & Joksimović, 2016;Lust et al, 2013b).…”

Section: Self-reported Measures and Learning Analyticsmentioning

confidence: 99%

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Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance

et al. 2017

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ABSTRACT. The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper focuses on the link between the learning strategies identified in the trace data and student reported approaches to learning. The paper reports on the findings of a study conducted in the scope of an undergraduate engineering course (N=144) that followed a flipped classroom design. The study found that learning strategies extracted from trace data can be interpreted in terms of deep and surface approaches to learning. The detected significant links with self-report measures are with small effect sizes for both the overall deep approach to learning scale and the deep strategy scale. However, there was no observed significance linking the surface approach to learning and surface strategy nor were there significant associations with motivation scales of approaches to learning. The significant effects on academic performance were found, and consistent with the literature that used self-report instruments showing that students who followed a deep approach to learning had a significantly higher performance.

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Section: Learning Analytics and Learning Strategymentioning

confidence: 99%

Section: Learning Analytics and Learning Strategymentioning

confidence: 99%

Section: Learning Analytics and Learning Theorymentioning

confidence: 99%

Section: Self-reported Measures and Learning Analyticsmentioning

confidence: 99%

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Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance

et al. 2017

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“…The study used analytic techniques to examine how participants engaged with the video case studies and associated online activities to provide insight into their learning behaviour and adopted learning strategies, and contribute to the revision of future program offerings. The incorporation of learning analytic techniques affords an opportunity to examine how users navigate the course activities and how they apply specific technologies to aid their learning progress (e.g., Jeong, Biswas, Johnson, & Howard, 2010;Lust, Elen, & Clarebout, 2013a, 2013b.…”

Section: Introductionmentioning

confidence: 99%

Identifying engagement patterns with video annotation activities: A case study in professional development

Mirriahi

Jovanović

Dawson

et al. 2018

AJET

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The rapid growth of blended and online learning models in higher education has resulted in a parallel increase in the use of audio-visual resources among students and teachers. Despite the heavy adoption of video resources, there have been few studies investigating their effect on learning processes and even less so in the context of academic development. This paper uses learning analytic techniques to examine how academic teaching staff engage with a set of prescribed videos and video annotations in a professional development course. The data was collected from two offerings of the course at a large research-intensive university in Australia. The data was used to identify patterns of activity and transition states as users engaged with the course videos and video annotations. Latent class analysis and hidden Markov models were used to characterise the evolution of engagement throughout the course. The results provide a detailed description of the evolution of learner engagement that can be readily translated into action aimed at increasing the quality of the learning experience.

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Analyzing the Group Formation Process in Intelligent Tutoring Systems

Rubio-Fernández

Muñoz‐Merino

Kloos

2019

Intelligent Tutoring Systems

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XI 3.2.3. Análisis de los grupos focales con los estudiantes․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․76 3.2.4. Conclusiones del análisis de las entrevistas y de los grupos focales․․․․․․․․․76 3.2.4.1. Entorno de aprendizaje adaptable y personalizado․․․․․․․․․․․․․․․․․․․․․․․․77 3.2.4.2. Mejora de la comunicación entre profesores y estudiantes․․․․․․․․․․․․․․․80 Capítulo 4. Propuesta de marco para la combinación de analítica de aprendizaje y la clase invertida․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․84 4.1. Indicadores utilizados en el marco de trabajo․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․86 4.1.1 Indicadores asociados con los vídeos․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․93 4.1.1.1. Indicadores asociados a un único estudiante․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․93 4.1.1.2. Indicadores asociados a varios estudiantes․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․94 4.1.2. Indicadores asociados con los ejercicios․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․101 4.1.2.1. Indicadores asociados a un único estudiante․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․101 4.1.2.2. Indicadores asociados a varios estudiantes․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․102 4.1.3. Indicadores asociados con las lecturas․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․107 4.1.4. Indicadores asociados con los foros de discusión․․․․․․․․․․․․․․․․․․․․․․․․․․․․․108 4.2. Uso de los indicadores en los diferentes tipos de clase invertida․․․․․․․․․․․․․․․․․109 4.2.1. Clase invertida orientada a ejercicios․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․110 4.2.1.1. Antes de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․113 4.2.1.2. Durante la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․114 4.2.1.3. Después de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․114 4.2.2. Clase invertida orientada a actividades en grupo․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․115 4.2.2.1. Antes de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․117 4.2.2.2. Durante la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․118 4.2.2.3. Después de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․118 4.2.3. Clase invertida orientada a prácticas experimentales․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․119 4.2.3.1. Antes de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․121 4.2.3.2. Durante la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․122 4.2.3.3. Después de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․123 4.2.4. Clase invertida orientada a la creación de recursos educativos por parte del _________estudiante․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․124 4.2.4.1. Antes de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․.126 4.2.4.2. Durante la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․.127 4.2.4.3. Después de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․127XII 4.2.5. Comparativa de los indicadores seleccionados según el modelo de clase _________invertida utilizado...

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Students’ tool-use within a web enhanced course: Explanatory mechanisms of students’ tool-use pattern

Cited by 61 publications

References 51 publications

Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance

Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance

Identifying engagement patterns with video annotation activities: A case study in professional development

Analyzing the Group Formation Process in Intelligent Tutoring Systems

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