Lecture Recordings, Viewing Habits, and Performance in an Introductory Programming Course

Picardo, Valerie; Denny, Paul; Luxton-Reilly, Andrew

doi:10.1145/3441636.3442307

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…These difficulties highlighted inequities in access to technology among our cohort, which were intensified by the additional challenges of the COVID-19 pandemic (Beaunoyer et al 2020; Cheshmehzangi et al 2022). Flexible learning has been identified as a way of overcoming educational and technological inequities, because it allows students to engage in their studies in the learning style, time, and location of their choice and accommodates individual experiences of illness and disability (Hollister et al 2022; Means and Neisler 2020; Muthuprasad et al 2021; Nkomo and Daniel 2021; Picardo et al 2021; Thompson et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Kay and Mann (2022) have advocated for the use of student video assignments and feedback videos to increase the effectiveness of communication between students and instructors and promote a deeper understanding of course content. However, Horn (2020) and Picardo and colleagues (2021) suggest that the use of recordings may exacerbate existing educational inequities and promote unhelpful student behaviors such as binge watching.…”

Section: Discussionmentioning

confidence: 99%

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Investigating the Effectiveness of Online Bioarchaeology Education through Participant Survey of a Cohort of International Adult Learners

Ward,

Barker,

Shoocongdej

et al. 2023

Adv. archaeol. pract.

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The COVID-19 pandemic triggered the adoption of online education across all sectors worldwide, which was particularly challenging for disciplines that rely on hands-on learning such as bioarchaeology. Although the impacts of this rapid transition have been well investigated in fields such as anatomy and forensic anthropology, there has been little research into its effects within bioarchaeology. We address this deficit by investigating two common perceptions around online learning from a bioarchaeological perspective: (1) online techniques are inadequate for teaching practical skills, and (2) online learning environments lack a sense of community, thereby negatively affecting learner experiences. To gauge learner perceptions around online practical education in this field, we conducted a qualitative survey of participants in a bioarchaeology masterclass series. Results suggest that students perceive online learning to be as effective for practical training as in-person alternatives and that online learning may engender a sense of community when offered using a collaborative, interactive approach. Based on our results we provide several key recommendations for online education in bioarchaeology, including an active emphasis on social engagement and relationship building, culturally appropriate teaching, and the use of resources to encourage flexibility in learning. A Thai-language abstract is available as Supplemental Text 1.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Investigating the Effectiveness of Online Bioarchaeology Education through Participant Survey of a Cohort of International Adult Learners

Ward,

Barker,

Shoocongdej

et al. 2023

Adv. archaeol. pract.

View full text Add to dashboard Cite

show abstract

“…The results from this investigation demonstrated that in-video quizzes were successful in creating an engaging and interactive mode of content delivery and student engagement with in-video questions was consistently high (71%-86%). Picardo et al (2021) investigated the common lecture recording viewing behaviors of students and the relationship between lecture recording viewing and academic performance in a first-year programming course. A significant positive correlation between lecture recording views and final grades was identified.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Influence of Students’ Modes of Behavioral Engagement in an Online Programming Course Using the Partial Least Squares Structural Equation Modeling Approach

2022

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Aim/Purpose: The goal of this study was twofold: first, to examine how learners’ behavioral engagement types affect their final grades in an online programming course; and second, to explore which factors most strongly affect student performance in an online programming course and their connection to the types of cognitive engagement. Background: During the COVID-19 pandemic situation, information technology educational methods and teaching have been transforming rapidly into online or blended. In this situation, students learn course content through digital learning management systems (LMSs), and the behavioral data derived from students’ interactions with these digital systems is important for instructors and researchers. However, LMSs have some limitations. For computer science students, the traditional learning management system is not enough because the coding behavior cannot be analyzed. Through the Opined platform, we collected log data from 217 undergraduates enrolled in a Python programming course offered by Feng Chia University in Taiwan in the spring semester of 2021. Methodology: We applied the evaluation framework of learning behavioral engagement conducted on a massive open online course (MOOC) platform and integrated it with the partial least squares structural equation modeling (PLS-SEM) approach. PLS-SEM is widely used in academic research and is appropriate for causal models and small sample sizes. Therefore, this kind of analysis is consistent with the purpose of our study. Contribution: In today’s fast-paced world of information technology, online learning is becoming an important form of learning around the world. Especially in computer science, programming courses teach many skills, such as problem-solving, teamwork, and creative thinking. Our study contributes to the understanding of how behavioral engagements in distance programming learning affect student achievement directly and through cognitive engagement. The results can serve as a reference for practitioners of distance programming education. Findings: Our results demonstrate that: (1) online time and video-watching constructs had significant effects on the self-assessment construct, self-assessment and video-watching constructs had significant effects on the final grade construct, and online document reading was not a significant factor in both self-assessments and final grades; (2) video watching had a most significant effect than other behavioral constructs in an online programming course; (3) cognitive engagement types are inextricably linked to the development of a behavioral engagement framework for online programming learning. The mediation analysis and the importance-performance map analysis supported the importance of cognitive engagement. Recommendations for Practitioners: (1) Online education platform developers and university policymakers should pay close attention to the development of self-assessment systems and design such systems based on students’ cognitive skills. (2) Instructors are advised to put substantial effort into the creation of videos for each course session and to actively promote students’ interest in the course material. Recommendation for Researchers: The empirical results reported in this study allow a better understanding of the connection between behavioral engagement and final achievement. However, there are still great challenges in trying to explore more kinds of engagement, like emotional or social engagement. It would be interesting to deepen the results obtained by integrating programming behavior like debugging and testing. Impact on Society: Online programming courses allow students to improve their coding skills and computer science background. Students’ behavioral engagement strongly affects their academic achievement, their ability to complete a course successfully, and the quality of the learning process. Our work can encourage more people who are different majors in society to learn coding in an online environment even not only computer science students. Moreover, the findings of this study can be recommendations for understanding students’ learning behavior and the development of distance programming learning. Future Research: We suggest for future studies: (1) include a wider range of participants, such as students enrolled in MOOCs environments; (2) include more log data items that can express various students’ behavior, depending on the reliability and validity of the research model; and (3) conduct more detailed studies of the effects of emotional engagement as well as additional aspects of students’ social engagement to elucidate the factors affecting students’ behavioral participation and performance more thoroughly.

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Computing Education Research in Australasia

Simón¹,

Sheard

Luxton-Reilly

et al. 2023

Past, Present and Future of Computing Education Research

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Lecture Recordings, Viewing Habits, and Performance in an Introductory Programming Course

Cited by 6 publications

References 16 publications

Investigating the Effectiveness of Online Bioarchaeology Education through Participant Survey of a Cohort of International Adult Learners

Investigating the Effectiveness of Online Bioarchaeology Education through Participant Survey of a Cohort of International Adult Learners

Exploring the Influence of Students’ Modes of Behavioral Engagement in an Online Programming Course Using the Partial Least Squares Structural Equation Modeling Approach

Computing Education Research in Australasia

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