A Multi-Modal Investigation of Self-Regulation Strategies Adopted by First-Year Engineering Students During Programming Tasks

Wert, Ethan; Grifski, Jeremy; Luo, Sijia; Atiq, Zahra

doi:10.1145/3446871.3469795

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…However, due to the lack of support and guidance in blended learning environments compared with traditional face-to-face teaching, learners often face difficulties that cannot be resolved, leading to low motivation and engagement, as well as decreased self-efficacy and failure (Kim et al, 2019; Kizilcec et al, 2017; Wert et al, 2021). To improve the situation, students typically use Google search engine to solve problems and difficulties encountered (Isda et al, 2021; Mracek, 2019).…”

Section: Related Workmentioning

confidence: 99%

Promoting Self-Regulation Progress and Knowledge Construction in Blended Learning via ChatGPT-Based Learning Aid

Lee

et al. 2023

Journal of Educational Computing Research

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This study combines ChatGPT, Apple’s Shortcuts, and LINE to create the ChatGPT-based Intelligent Learning Aid (CILA), aiming to enhance self-regulation progress and knowledge construction in blended learning. CILA offers real-time, convergent information to learners’ inquiries, as opposed to traditional Google search engine that provide divergent information. By addressing questions promptly, CILA minimizes interruptions during the performance phase of self-regulation progress. The tool records learners’ questions and answers, aiding self-reflection in self-regulation progress. We evaluated self-regulation progress using motivation, engagement, and self-efficacy as indicators. Findings show that CILA’s intervention effectively improves self-regulation progress and knowledge construction, offering benefits over divergent information in blended learning contexts with respect to amotivation, intrinsic motivation, and behavioral engagement. This research highlights the potential of incorporating large language models like ChatGPT in educational settings to support teachers and students.

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Section: Related Workmentioning

confidence: 99%

Promoting Self-Regulation Progress and Knowledge Construction in Blended Learning via ChatGPT-Based Learning Aid

Lee

et al. 2023

Journal of Educational Computing Research

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“…The students had been taught a process for debugging prior to undertaking the debugging tasks. Like Wert et al [90], the authors reported that many students had strong emotional reactions to debugging, especially when they encountered difficulties. Wert et al [90] went on to highlight persistence when programming as a key factor contributing to success for novices.…”

Section: Self-regulation and Debuggingmentioning

confidence: 90%

“…Like Wert et al [90], the authors reported that many students had strong emotional reactions to debugging, especially when they encountered difficulties. Wert et al [90] went on to highlight persistence when programming as a key factor contributing to success for novices. Students react to challenges and associated negative emotions differently, falling into one of two categories: either they persist (are "movers" [72]) or they give up (are "stoppers" [72]).…”

Section: Self-regulation and Debuggingmentioning

confidence: 90%

A Think-Aloud Study of Novice Debugging

Whalley

Settle

Luxton-Reilly

2023

ACM Trans. Comput. Educ.

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Debugging is a core skill required by programmers, yet we know little about how to effectively teach the process of debugging. The challenges of learning debugging are compounded for novices who lack experience and are still learning the tools they need to program effectively. In this work, we report a case study in which we used a think-aloud protocol to gain insight into the behaviour of three students engaged in debugging tasks. Our qualitative analysis reveals a variety of helpful practices and barriers that limit the effectiveness of debugging. We observe that comprehension, evidence-based activities, and workflow practices all contribute to novice debugging success. Lack of sustained effort, precision, and methodical processes negatively impact debugging effectiveness. We anticipate that understanding how students engage in debugging tasks will aid future work to address ineffective behaviours and promote effective debugging activities.

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“…Recent work by Atiq (2018) and colleagues (Wert et al, 2021) used multi‐modal approaches to study students' emotions during programming tasks. In Atiq's studies, students' synchronized biometric data (i.e., facial expressions, eye gaze tracking, electrodermal activity) were measured using the iMotions platform (iMotions, 2015) along with students' self‐report of their emotions via the Achievement Emotions Questionnaire instrument (Pekrun et al, 2011) and a short post‐task interview.…”

Section: Examples From Eer and Other Fieldsmentioning

confidence: 99%

Situating multi‐modal approaches in engineering education research

Villanueva

Anwar

2022

J of Engineering Edu

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BACKGROUNDTraditionally, engineering education researchers rely on quantitative, qualitative, and mixed-or multi-method approaches for their research designs, each with its nuances, set of rules, and worldviews. Here we present a new approach that is yet to be widely accepted in engineering education research (EER): namely the multi-modal approach. This guest editorial is particularly timely because, in 2020, the EER taxonomy underwent a revision (Version 1.2), where the term "multi-modal approaches" was added to Section 12.d.iv (Finelli, 2020). With this addition to the taxonomy, it is important for EER scholars to understand what multi-modal approaches entail and how they are different from other approaches present in mixed-methods or multi-methods. To clarify this new approach further, we give examples of how multi-modal research is used in EER and other related studies. | INTRODUCTIONRooted in sociolinguistic approaches, multi-modality centers on multiple modes of communication and representation (e.g., reading, writing, and speech) to study meaning-making. In multi-modality, meaning-making includes multiple dynamics or representations of phenomena (or layers) and sensing modes by which an individual recognizes or becomes aware of the complexity of experienced phenomena (e.g., Ledin & Machin, 2017). Multiple sensing modalities are what give an individual "a wealth of information to support interaction with the world and with one another" (Turk, 2014, p. 189), and these can be interpreted through senses such as, but not limited to, hearing, seeing, touching, and feeling (e.g., Ledin & Machin, 2017). Phenomena, in the multi-modal sense, include the "binding of inputs from multiple sensory modalities and the effects of this binding" (Lachs, 2017) on individuals and accounts for the influence that one sensory modality has on another (Spence et al., 2009). Taken together, multi-modal approaches aim to capture the multi-layered and near-real-time ensemble of the intersections and dynamics of meaning-making (e.g., Archer & Newfield, 2014).Take, for example, the experience of listening to a song. When a person listens to a song, multiple events happen almost simultaneously: a person hears the song, reads the song lyrics, and thinks about how the song applies to their life. If the song connects with a person on a deeper level, their emotions manifest as excitement or joy, their heart palpitates, their mood changes, and they almost immediately begin to memorize the lyrics and tunes. Analogously, multimodal approaches try to collect many simultaneous events in order to represent the messiness and immediacy of life.Multiple layers of the phenomena include both the context and nature of discursive practices while also accounting for the convergence and divergence of its sensing modalities (Archer & Newfield, 2014). Together, the layers and immediacies of multi-modal approaches allow scholars to further nuances in meaning-making as it relates to complex realities such as access, social justice, and equity (

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A Multi-Modal Investigation of Self-Regulation Strategies Adopted by First-Year Engineering Students During Programming Tasks

Cited by 9 publications

References 6 publications

Promoting Self-Regulation Progress and Knowledge Construction in Blended Learning via ChatGPT-Based Learning Aid

Promoting Self-Regulation Progress and Knowledge Construction in Blended Learning via ChatGPT-Based Learning Aid

A Think-Aloud Study of Novice Debugging

Situating multi‐modal approaches in engineering education research

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