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Hands-on experiments using the Low-Cost Desktop Learning Modules (LCDLMs) have been implemented in dozens of classrooms to supplement student learning of heat transfer and fluid mechanics concepts with students of varying prior knowledge. The prior knowledge of students who encounter these LCDLMs in the classroom may impact the degree to which students learn from these interactive pedagogies. This paper reports on the differences in student cognitive learning between groups with low and high prior knowledge of the concepts that are tested. Student conceptual test results for venturi, hydraulic loss, and double pipe heat exchanger LCDLMs are analyzed by grouping the student data into two bins based on pre-test score, one for students scoring below 50% and another for those scoring above and comparing the improvement from pretest to posttest between the two groups. The analysis includes data from all implementations of each LCDLM for the 2020-2021 school year. Results from each of the three LCDLMs were analyzed separately to compare student performance on different fluid mechanics or heat exchanger concepts. Then, the overall pre- and posttest scores for all three LCDLMs were analyzed to examine how this interactive pedagogy impacts cognitive gains. Results showed statistically significant differences in improvement between low prior knowledge groups and high prior knowledge groups. Additional findings showed statistically significant results suggesting that the gaps in performance between low prior knowledge and high prior knowledge groups on pre-tests for the LCDLMs were decreased on the posttest. Findings showed that students with lower prior knowledge show a greater overall improvement in cognitive gains than those with higher prior knowledge on all three low-cost desktop learning modules.
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A comparison of different machine learning algorithms for predicting student performance in an online interactive mathematics game.
This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students’ (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2) what types of in-game features (i.e. student in-game behaviors, math anxiety, mathematical strategies) were associated with student math knowledge scores. The results indicated that the Random Forest algorithm showed the best performance (i.e. the accuracy of models, error measures) in predicting posttest math knowledge scores among the seven algorithms employed. Out of 37 features included in the model, the validity of the students’ first mathematical transformation was the most predictive of their posttest math knowledge scores. Implications for game learning analytics and supporting students’ algebraic learning are discussed based on the findings.
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- Award ID(s):
- 2142984
- PAR ID:
- 10415168
- Date Published:
- Journal Name:
- Interactive learning environments
- ISSN:
- 1049-4820
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Hands-on experiments using the Low-Cost Desktop Learning Modules (LCDLMs) have been implemented in dozens of classrooms to supplement student learning of heat transfer and fluid mechanics concepts with students of varying prior knowledge. The prior knowledge of students who encounter these LCDLMs in the classroom may impact the degree to which students learn from these interactive pedagogies. This paper reports on the differences in student cognitive learning between groups with low and high prior knowledge of the concepts that are tested. Student conceptual test results for venturi, hydraulic loss, and double pipe heat exchanger LCDLMs are analyzed by grouping the student data into two bins based on pre-test score, one for students scoring below 50% and another for those scoring above and comparing the improvement from pretest to posttest between the two groups. The analysis includes data from all implementations of each LCDLM for the 2020-2021 school year. Results from each of the three LCDLMs were analyzed separately to compare student performance on different fluid mechanics or heat exchanger concepts. Then, the overall pre- and posttest scores for all three LCDLMs were analyzed to examine how this interactive pedagogy impacts cognitive gains. Results showed statistically significant differences in improvement between low prior knowledge groups and high prior knowledge groups. Additional findings showed statistically significant results suggesting that the gaps in performance between low prior knowledge and high prior knowledge groups on pre-tests for the LCDLMs were decreased on the posttest. Findings showed that students with lower prior knowledge show a greater overall improvement in cognitive gains than those with higher prior knowledge on all three low-cost desktop learning modules.more » « less
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Despite theoretical benefits of replayability in educational games, empirical studies have found mixed evidence about the effects of replaying a previously passed game (i.e., elective replay) on students’ learning. Particularly, we know little about behavioral features of students’ elective replay process after experiencing failures (i.e., interruptive elective replay) and the relationships between these features and learning outcomes. In this study, we analyzed 5th graders’ log data from an educational game, ST Math, when they studied fractions—one of the most important but challenging math topics. We systematically constructed interruptive elective replay features by following students’ sequential behaviors after failing a game and investigated the relationships between these features and students’ post-test performance, after taking into account pretest performance and in-game performance. Descriptive statistics of the features we constructed revealed individual differences in the elective replay process after failures in terms of when to start replaying, what to replay, and how to replay. Moreover, a Bayesian multi-model linear regression showed that interruptive elective replay after failures might be beneficial for students if they chose to replay previously passed games when failing at a higher, more difficult level in the current game and if they passed the replayed games.more » « less
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Our team has developed Low-Cost Desktop Learning Modules (LCDLMS) as tools to study transport phenomena aimed at providing hands-on learning experiences. With an implementation design embedded in the community of inquiry framework, we disseminate units to professors across the country and train them on how to facilitate teacher presence in the classroom with the LC-DLMs. Professors are briefed on how create a homogenous learning environment for students based on best-practices using the LC-DLMs. By collecting student cognitive gain data using pre/posttests before and after students encounter the LC-DLMs, we aim to isolate the variable of the professor on the implementation with LC-DLMs. Because of the onset of COVID-19, we have modalities for both hands-on and virtual implementation data. An ANOVA whereby modality was grouped and professor effect was the independent variable had significance on the score difference in pre/posttest scores (p<0.0001) and on posttest score only (p=0.0004). When we divide out modality between hands-on and virtual, an ANOVA with an Ftest using modality as the independent variable and professor effect as the nesting variable also show significance on the score difference between pre and posttests (p-value=0.0236 for handson, and p-value=0.0004 for virtual) and on the posttest score only (p-value=0.0314 for hands-on, and p-value<0.0001 for virtual). These results indicate that in all modalities professor had an effect on student cognitive gains with respect to differences in pre/posttest score and posttest score only. Future will focus on qualitative analysis of features of classrooms yield high cognitive gains in undergraduate engineering students.more » « less
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