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Procrastination is a major issue faced by students which can lead to negative impacts on their academic performance and mental health. Productivity tools aim to help individuals to alleviate this behavior by providing self-regulatory support. However, the processes of how these applications help students conquer academic procrastination are under-explored. Particularly, it is essential to understand what aspects of these applications help which kinds of students in accomplishing their academic tasks. In this paper, we address this gap by presenting an academic planning and time management app (Proccoli) and a study designed to understand the association between student procrastination modeling, in-app behaviors, and perceived performance with app evaluation. As the core of our study, we analyze student perceptions of Proccoli and its impact on their study tasks and time management skills. Then, we model student procrastination behaviors by Hawkes process mining, assess student in-app behaviors by specifying planning and performance-related measures and evaluate the relationship between student behaviors and the evaluation survey results. Our study shows a need for personalized self-regulation support in Proccoli, as students with different in-app studying behaviors are found to have different perceptions of the app functionalities and the association between the prompts for social accountability students received by using Proccoli and their procrastination behavior is significant.
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Reciprocal Relations Between Students’ Evaluation, Reformulation Behaviors, and Engineering Design Performance Over Time
Understanding the design process may reveal when and where resources should be focused and how engineers can better use tools, methods, and techniques to enhance the quality of designs and creative performance. The literature suggests the importance of iterative evaluation and reformulation in the engineering design process. The current study collected 111 high school students’ logs of designing an energy-saving house in Energy3D, a computer-aided design environment. Using a cross-lag model, we investigated the reciprocal relationship between students’ evaluation and reformulation behaviors and how these behaviors influence students’ design performance at the early, middle, and final design stages. The results suggest that there is a positive predictive relationship between students’ evaluation and reformulation process; reformulation positively predicts design performance and mediates the relationship between evaluation and design performance across time. These results provide empirical evidence of the importance of iterative evaluation and reformulation in the design process and implications for teachers and system designers to support students’ design.
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- Award ID(s):
- 2105695
- PAR ID:
- 10213976
- Date Published:
- Journal Name:
- Journal of Science Education and Technology
- ISSN:
- 1059-0145
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Research on self-regulated learning (SRL) in engineering design is growing. While SRL is an effective way of learning, however, not all learners can regulate themselves successfully. There is a lack of research regarding how student characteristics, such as science knowledge and design knowledge, interact with SRL. Adapting the SRL theory in the field of engineering design, this study proposes a research model to examine the mediation and causal relationships among science knowledge, design knowledge, and SRL activities (i.e. observation, formulation, reformulation, analysis, evaluation). Partial least squares modeling was utilized to examine how the science and design knowledge of 108 ninth-grade participants interacted with their SRL activities in the process of performing an engineering task. Results reveal that prior science and design knowledge positively predict SRL activities. They also show that reformulation and analysis are the two SRL activities that can lead to an improvement in post science and design knowledge, but excessive observation can hinder post design knowledge. These results have important implications for the construction of learning environments to support SRL based on students’ prior knowledge levels.more » « less
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Self-regulation is crucial for student success in scientific inquiry and engineering design. However, it remains unclear how students dynamically engage in self-regulated learning (SRL) processes to achieve high performance. In this study, we investigated the temporal nature of self-regulation during engineering design by leveraging computer trace data from 101 high school students who designed an energy-plus house in a simulated learning environment. Using sequential mining, we found that high-performing students were more engaged in the Observation, Analysis, and Evaluation phases of SRL than low-performing students. Additionally, high-performing students demonstrated consecutive sequential patterns between Observation and Analysis, Reformation and Evaluation, and Analysis and Evaluation behaviors. These findings provide insights into students’ SRL processes and the design of scaffoldings.more » « less
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Grieff, S. (Ed.)Recently there has been increased development of curriculum and tools that integrate computing (C) into Science, Technology, Engineering, and Math (STEM) learning environments. These environments serve as a catalyst for authentic collaborative problem-solving (CPS) and help students synergistically learn STEM+C content. In this work, we analyzed students’ collaborative problem-solving behaviors as they worked in pairs to construct computational models in kinematics. We leveraged social measures, such as equity and turn-taking, along with a domain-specific measure that quantifies the synergistic interleaving of science and computing concepts in the students’ dialogue to gain a deeper understanding of the relationship between students’ collaborative behaviors and their ability to complete a STEM+C computational modeling task. Our results extend past findings identifying the importance of synergistic dialogue and suggest that while equitable discourse is important for overall task success, fluctuations in equity and turn-taking at the segment level may not have an impact on segment-level task performance. To better understand students’ segment-level behaviors, we identified and characterized groups’ planning, enacting, and reflection behaviors along with monitoring processes they employed to check their progress as they constructed their models. Leveraging Markov Chain (MC) analysis, we identified differences in high- and low-performing groups’ transitions between these phases of students’ activities. We then compared the synergistic, turn-taking, and equity measures for these groups for each one of the MC model states to gain a deeper understanding of how these collaboration behaviors relate to their computational modeling performance. We believe that characterizing differences in collaborative problem-solving behaviors allows us to gain a better understanding of the difficulties students face as they work on their computational modeling tasks.more » « less
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Context Engineering design skills are essential for engineering students to succeed in their careers. Engineering design is a skill that is in high demand in the current job market and should be prioritized in education. Purpose While design has been acknowledged as a cognitive skill in research, there exists limited literature addressing the cognitive foundations of design thinking. Hence, engineering educators must understand the engineering design process, as well as the different ways students approach design problem-solving and the potential reason behind these differences. To understand how people solve design problems, we need to consider how their minds work and the strategies they use. Spatial ability stands out as a cognitive factor that is crucial for designers and holds significance in well-established theories and models of intelligence. However, to date, research exploring the impact of spatial ability on design thinking and its influence on problem-scoping behaviors remains limited. This paper examines how engineering students’ spatial skills influence how they define the scope of open-ended design problems. The central research question that guides this paper is “How do design problem-scoping behaviors differ for engineering students based on their spatial scores?”. Methods The researchers used a mixed methods research approach to answer their research question, collecting qualitative and quantitative data in two phases. One hundred twenty-seven undergraduate engineering students completed four tests that measure spatial reasoning skills in the quantitative phase and 101 students returned to finish the three design tasks in the second phase. This paper will examine the performance of students with low spatial and high spatial skills on one of the completed design tasks. Outcomes From the study, it was clear that spatial skills have an impact on the design-scoping behaviors of the undergraduate engineering students. It was inferred that high spatial skill visualizers emphasized the technical details of the design problem whereas low spatial skill visualizers emphasized the context of the design problem during their problem-scoping behavior. A Mann-Whitney test revealed there was a statistically significant difference in detail- and context-focused segments between the high and low spatial visualizer groups. Conclusion This research study confirms that a relationship exists between spatial and design skills. The study also found that undergraduate engineering students with different levels of spatial skills had different approaches to scoping design problems.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10956-021-09906-3
