A tale of two guessing strategies: interpreting the time students spend solving problems through online log data

Guthrie, Matthew W.; Zhang, Tom; Chen, Zhongzhou

doi:10.1119/perc.2020.pr.guthrie

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…In two earlier studies on OLMs 15,20 , those types of engaged behaviors are observed to be more frequently observed among higher performing students. Behaivor (a) was indicative of higher incoming knowledge, whereas behavior (b) was often associated with students who needs to learn the material, and more often observed on harder modules.…”

Section: Engaged Modulesmentioning

confidence: 78%

Analyzing the Heterogeneous Impact of Remote Learning on Students' Ability to Stay on Track During the Pandemic

Chen,

Zhang

2021

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This study investigates how remote learning due to the COVID pandemic impacts students' ability to keep up with the pace of instruction in a university level physics course, with a focus on the heterogeneous impact of remote learning on different demographic groups. Student learning data is collected from 70 online learning modules assigned as both online homework and self-study material in both Fall 2020 and Spring 2020 semesters, with the first 41 modules being assigned before campus closure in Spring 2020. Students' ability to stay on track is measured by three data indicators: percentage of modules submitted before the due date, percentage of modules submitted early for extra credit, and percentage of modules properly engaged with. The student population is divided into two demographic groups according to each of the four demographic variables: Gender, Ethnicity, Transfer Status and First generation status. All three data indicators from both semesters are first compared between each pair of demographic groups within the same semester, and then compared within the same group between the two semesters. Exam and course scores are also compared between demographics groups in Fall 2020 and against scores from the Fall 2019 semester. The analysis revealed that remote learning significantly reduced on-time and early submission for first-generation students, but increased the frequency of early submission for transfer students. Under-represented minority students had higher percentage of engaged modules during the pandemic, while non-URM students had the same level of module engagement before and after the pandemic. These results suggest that pandemic remote learning had very different impact for students with different demographic background, and future instructional design should strive to provide more flexible options to meet the various needs for a diverse higher-ed student population.

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Section: Engaged Modulesmentioning

confidence: 78%

Analyzing the Heterogeneous Impact of Remote Learning on Students' Ability to Stay on Track During the Pandemic

Chen,

Zhang

2021

Preprint

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“…In the current study, we fit the log duration of each assessment attempt using either normal or skewed distribution models using the R package mixsmsn ( Prates and Cabral, 2009 ), following the fitting procedure described in detail in the appendix of a previous study ( Chen et al, 2020 ). In the case when a single component distributed was the best fit for the duration, the cutoff was set as either 2 standard deviations below the mean duration, or 15 s, whichever was longer ( Guthrie et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining

et al. 2022

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Success in online and blended courses requires engaging in self-regulated learning (SRL), especially for challenging STEM disciplines, such as physics. This involves students planning how they will navigate course assignments and activities, setting goals for completion, monitoring their progress and content understanding, and reflecting on how they completed each assignment. Based on Winne & Hadwin’s COPES model, SRL is a series of events that temporally unfold during learning, impacted by changing internal and external factors, such as goal orientation and content difficulty. Thus, as goal orientation and content difficulty change throughout a course, so might students’ use of SRL processes. This paper studies how students’ SRL behavior and achievement goal orientation change over time in a large (N = 250) college introductory level physics course taught online. Students’ achievement goal orientation was measured by repeated administration of the achievement goals questionnaire-revised (AGQ-R). Students’ SRL behavior was measured by analyzing their clickstream event traces interacting with online learning modules via a combination of trace clustering and process mining. Event traces were first divided into groups similar in nature using agglomerative clustering, with similarity between traces determined based on a set of derived characteristics most reflective of students’ SRL processes. We then generated causal nets for each cluster of traces via process mining and interpreted the underlying behavior and strategy of each causal net according to the COPES SRL framework. We then measured the frequency at which students adopted each causal net and assessed whether the adoption of different causal nets was associated with responses to the AGQ-R. By repeating the analysis for three sets of online learning modules assigned at the beginning, middle, and end of the semester, we examined how the frequency of each causal net changed over time, and how the change correlated with changes to the AGQ-R responses. Results have implications for measuring the temporal nature of SRL during online learning, as well as the factors impacting the use of SRL processes in an online physics course. Results also provide guidance for developing online instructional materials that foster effective SRL for students with different motivational profiles.

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“…The mandatory first AC attempt on each OLM requires students to plan on whether to engage with the problems or randomly submit a response without reading and proceeding to the IC. Previous studies [6,12] suggest that attempts submitted under 15 seconds are likely generated by students who skipped reading the problems in the AC, whereas attempts between 15 and 35 seconds are likely generated by students who read the problems but didn't know how to solve them properly. Attempts longer than 35 seconds have a higher probability of being a genuine attempt at solving the AC problems and are more frequently observed among high performing students.…”

Section: Sequencementioning

confidence: 99%

Measuring the Impact of COVID-19 Induced Campus Closure on Student Self-Regulated Learning in Physics Online Learning Modules

Zhang

Taub

Chen

2021

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This paper examines the impact of COVID-19 induced campus closure on university students' self-regulated learning behavior by analyzing click-stream data collected from student interactions with 70 online learning modules in a university physics course. To do so, we compared the trend of six types of actions related to the three phases of self-regulated learning before and after campus closure and between two semesters. We found that campus closure changed students' planning and goal setting strategies for completing the assignments, but didn't have a detectable impact on the outcome or the time of completion, nor did it change students' self-reflection behavior. The results suggest that most students still manage to complete assignments on time during the pandemic, while the design of online learning modules might have provided the flexibility and support for them to do so.

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A tale of two guessing strategies: interpreting the time students spend solving problems through online log data

Cited by 8 publications

References 11 publications

Analyzing the Heterogeneous Impact of Remote Learning on Students' Ability to Stay on Track During the Pandemic

Analyzing the Heterogeneous Impact of Remote Learning on Students' Ability to Stay on Track During the Pandemic

Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining

Measuring the Impact of COVID-19 Induced Campus Closure on Student Self-Regulated Learning in Physics Online Learning Modules

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