A constructivist approach in a blended e-learning environment for statistics

Poelmans, Stephan; Wessa, Patrick

doi:10.1080/10494820.2013.766890

Cited by 26 publications

(27 citation statements)

References 35 publications

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“…Exploring perspectives on personalization of feedback by means of studies at individual rather than group level can be another perspective using additional experimental variables. Dimension to consider are gender, previous knowledge, the level of computer self-efficacy level in terms of their ability to learn and use software (Compeau & Higgins, 1995;Davis, 1989;Davis, Bagozzi, & Warshaw, 1989;Keller, 2009;Poelmans & Wessa, 2013;Venkatesh, Morris, Davis, & Davis, 2003).…”

Section: Resultsmentioning

confidence: 99%

Process-mining enabled feedback: “Tell me what I did wrong” vs. “tell me how to do it right”

Sedrakyan

Weerdt

Snoeck

2016

Computers in Human Behavior

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Fast advancement of technology has led to an increased interest for using information technology to provide feedback based on learning behavior observations. This work outlines a novel approach for analyzing behavioral learner data through the application of process mining techniques specifically targeting a complex problem solving process. We realize this in the context of one particular learning case, namely, domain modeling. This work extends our previous research on process-mining analysis of domain modeling behavior of novices by elaborating with new insights from a replication study enhanced with an extra observation on how novices verify/validate models. The findings include a set of typical modeling and validation patterns that can be used to improve teaching guidance for domain modeling courses. From a scientific viewpoint, the results contribute to improving our knowledge on the cognitive aspects of problem-solving behavior of novices in the area of domain modeling, specifically regarding process-oriented feedback as opposed to traditional outcome feedback (is a solution correct? Why (not)?) usually applied in this type of courses. Ultimately, the outcomes of the work can be inspirational outside of the area of domain modeling as learning event data is becoming readily available through virtual learning environments and other information systems.

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Section: Resultsmentioning

confidence: 99%

Process-mining enabled feedback: “Tell me what I did wrong” vs. “tell me how to do it right”

Sedrakyan

Weerdt

Snoeck

2016

Computers in Human Behavior

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“…BL aims to incorporate the advantages of both face-to-face learning and e-learning (Graham 2006) by exploring the possibilities to effectively combine both (Wong et al 2014). Some of the benefits of BL include greater flexibility in terms of costs (Harding et al 2012), accommodation of a large number of students (Macedo-Rouet et al 2009), increased motivation and performance (Dziuban et al 2011), and flexibility and innovation in teaching and learning (Poelmans and Wessa 2015). Studies that discussed the paradigm shift that BL entails in learners' independence and autonomy as well as approaches in pedagogy and curriculum design to effectively employ BL are evident in literature (Lai et al 2016;Wong et al 2020).…”

Section: Emergency Remote Learningmentioning

confidence: 99%

Why learning space matters: a script approach to the phenomena of learning in the emergency remote learning scenario

Chattaraj

Vijayaraghavan

2021

J. Comput. Educ.

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The study focuses on how the notion of learning space is perceived and experienced by learners in the Emergency Remote Learning (ERL) scenario. In doing so, the lived experiences of remote learners who were abruptly shifted to a completely online learning space due to the pandemic COVID-19 in the Indian higher education system are documented. Online interviews were conducted with eight undergraduate and four postgraduate students of English and Cultural Studies, enrolled at a Southern Indian university, and their responses were explicated using the Interpretive Phenomenological Analysis (IPA) approach. The interviews revealed that the phenomenon of ERL is shaped by dissonance informed by the absence of a familiar learning space. Often the patterns of this dissonance were marked by (1) the perception of learning and learning space, (2) the lack of intimacy in learning and learning space, (3) the negotiations made for learning and the space of learning in ERL, and (4) the challenges to cope with the responsibilities of the ERL scenario. Further, the script approach was applied to analyse the data and the analysis revealed an expansion of the existing internal scripts that were based on previous learning experiences of the learners. The study thus establishes the centrality of space in the process of learning and points out how the lack of a familiar learning space is linked to the absence of internal scripts that considerably impact learning. The study concludes by discussing the possibilities of application of script approach to effectively incorporate the aspect of learning space in new pedagogies and learning models as Blended Learning (BL) and Online Learning (OL) become the new normal worldwide.

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“…Individual rather than group works can be studied to observe the effects from personal characteristics that might be relevant (e.g. for personalization of feedback) such as gender, previous knowledge of data modeling, the level of computer self-efficacy level in terms of their ability to learn and use a computer software and general ICT experience in terms of previous programming experience (Compeau & Higgins, 1995;Davis, 1989;Davis, Bagozzi, & Warshaw, 1989;Keller, 2009;Poelmans & Wessa, 2013;Venkatesh, Morris, Davis, & Davis, 2003). Finally, comparison between novices and experts rather than best/worst solutions could provide more insights on modeling patterns.…”

Section: Limitationsmentioning

confidence: 99%

Process mining analysis of conceptual modeling behavior of novices – empirical study using JMermaid modeling and experimental logging environment

Sedrakyan

Snoeck

Weerdt

2014

Computers in Human Behavior

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a b s t r a c tPrevious studies on learning challenges in the field of modeling focus on cognitive perspectives, such as model understanding, modeling language knowledge and perceptual properties of graphical notation by novice business analysts as major sources affecting model quality. In the educational context outcome feedback is usually applied to improve learning achievements. However, not many research publications have been written observing the characteristics of a modeling process itself that can be associated with better/worse learning outcomes, nor have any empirically validated results been reported on the observations of modeling activities in the educational context. This paper attempts to cover this gap for conceptual modeling. We analyze modeling behavior (conceptual modeling event data of 20 cases, 10.000 events in total) using experimental logging functionality of the JMermaid modeling tool and process mining techniques. The outcomes of the work include modeling patterns that are indicative for worse/better learning performance. The results contribute to (1) improving teaching guidance for conceptual modeling targeted at process-oriented feedback, (2) providing recommendations on the type of data that can be useful in observing a modeling behavior from the perspective of learning outcomes. In addition, the study provides first insights for learning analytics research in the domain of conceptual modeling.

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A constructivist approach in a blended e-learning environment for statistics

Cited by 26 publications

References 35 publications

Process-mining enabled feedback: “Tell me what I did wrong” vs. “tell me how to do it right”

Process-mining enabled feedback: “Tell me what I did wrong” vs. “tell me how to do it right”

Why learning space matters: a script approach to the phenomena of learning in the emergency remote learning scenario

Process mining analysis of conceptual modeling behavior of novices – empirical study using JMermaid modeling and experimental logging environment

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