Pub Date : 2022-12-24DOI: 10.1177/07356331221144074
Hazra Imran
Adding gaming elements to conventional teaching methodologies has gained a lot of attention because of its ability to incorporate an engaging, motivating, and fun-based environment. As a result, learners' dedication and performance are also better. Unfortunately, current gamification models do not consider the effect of different levels of gamification. Therefore, this study provides deeper insight into the three levels of gamification on the motivation, engagement, and performance of 450 undergraduates enrolled in an online course. The level of gamification is experimentally manipulated based on different gaming elements and the presentation of learning content. The outcomes were measured at three points. Quantitative methods were used to analyze defined measures, and qualitative methods were used to analyze open-ended measures. The results revealed no change in outcomes between all groups during pre-course and mid-course assessments. However, motivation, engagement, and performance are improved in gamified environments, and these effects are more noticeable towards the end of the course. It was discovered that the gamification level was a significant determinant of motivation and performance but not engagement, which highlights the importance of implementing gamification in educational platforms. The gamification appeared to be a pedagogically profound way of engaging students in the online course. The whole setup triggered the learner’s motivation to learn and perform in the course. We conclude that gamification does help in motivation, engagement and performance if considered properly. Thus, educators and educational institutions seeking to enhance student motivation and performance may look at the ‘right level’ of gamification as an appropriate methodology.
{"title":"An Empirical Investigation of the Different Levels of Gamification in an Introductory Programming Course","authors":"Hazra Imran","doi":"10.1177/07356331221144074","DOIUrl":"https://doi.org/10.1177/07356331221144074","url":null,"abstract":"Adding gaming elements to conventional teaching methodologies has gained a lot of attention because of its ability to incorporate an engaging, motivating, and fun-based environment. As a result, learners' dedication and performance are also better. Unfortunately, current gamification models do not consider the effect of different levels of gamification. Therefore, this study provides deeper insight into the three levels of gamification on the motivation, engagement, and performance of 450 undergraduates enrolled in an online course. The level of gamification is experimentally manipulated based on different gaming elements and the presentation of learning content. The outcomes were measured at three points. Quantitative methods were used to analyze defined measures, and qualitative methods were used to analyze open-ended measures. The results revealed no change in outcomes between all groups during pre-course and mid-course assessments. However, motivation, engagement, and performance are improved in gamified environments, and these effects are more noticeable towards the end of the course. It was discovered that the gamification level was a significant determinant of motivation and performance but not engagement, which highlights the importance of implementing gamification in educational platforms. The gamification appeared to be a pedagogically profound way of engaging students in the online course. The whole setup triggered the learner’s motivation to learn and perform in the course. We conclude that gamification does help in motivation, engagement and performance if considered properly. Thus, educators and educational institutions seeking to enhance student motivation and performance may look at the ‘right level’ of gamification as an appropriate methodology.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"61 1","pages":"847 - 874"},"PeriodicalIF":4.8,"publicationDate":"2022-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43369015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-17DOI: 10.1177/07356331221127300
Nasha Zhai, Xiaomei Ma
Automated writing evaluation (AWE) has been frequently used to provide feedback on student writing. Many empirical studies have examined the effectiveness of AWE on writing quality, but the results were inconclusive. Thus, the magnitude of AWE’s overall effect and factors influencing its effectiveness across studies remained unclear. This study re-examined the issue by meta-analyzing the results of 26 primary studies with a total of 2468 participants from 2010 to 2022. The results revealed that AWE had a large positive overall effect on writing quality (g = 0.861, p < 0.001). Further moderator analyses indicated that AWE was more effective for post-secondary students than for secondary students and had more benefits for English as a Foreign Language (EFL) and English as a Second Language (ESL) learners than for Native English Speaker (NES) learners. When the genre of writing was considered, AWE showed a more significant impact on argumentative writing than on academic and mixed writing genres. However, intervention duration, feedback combination, and AWE platform did not moderate the effect of AWE on writing quality. The implications and recommendations for both research and practice are discussed in depth.
{"title":"The Effectiveness of Automated Writing Evaluation on Writing Quality: A Meta-Analysis","authors":"Nasha Zhai, Xiaomei Ma","doi":"10.1177/07356331221127300","DOIUrl":"https://doi.org/10.1177/07356331221127300","url":null,"abstract":"Automated writing evaluation (AWE) has been frequently used to provide feedback on student writing. Many empirical studies have examined the effectiveness of AWE on writing quality, but the results were inconclusive. Thus, the magnitude of AWE’s overall effect and factors influencing its effectiveness across studies remained unclear. This study re-examined the issue by meta-analyzing the results of 26 primary studies with a total of 2468 participants from 2010 to 2022. The results revealed that AWE had a large positive overall effect on writing quality (g = 0.861, p < 0.001). Further moderator analyses indicated that AWE was more effective for post-secondary students than for secondary students and had more benefits for English as a Foreign Language (EFL) and English as a Second Language (ESL) learners than for Native English Speaker (NES) learners. When the genre of writing was considered, AWE showed a more significant impact on argumentative writing than on academic and mixed writing genres. However, intervention duration, feedback combination, and AWE platform did not moderate the effect of AWE on writing quality. The implications and recommendations for both research and practice are discussed in depth.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"61 1","pages":"875 - 900"},"PeriodicalIF":4.8,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46870200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.1177/07356331221115663
Xuesong Zhai, Jiaqi Xu, Nian-Shing Chen, Jun Shen, Yan Li, Yonggu Wang, Xiaoyan Chu, Yumeng Zhu
Affective computing (AC) has been regarded as a relevant approach to identifying online learners’ mental states and predicting their learning performance. Previous research mainly used one single-source data set, typically learners’ facial expression, to compute learners’ affection. However, a single facial expression may represent different affections in various head poses. This study proposed a dual-source data approach to solve the problem. Facial expression and head pose are two typical data sources that can be captured from online learning videos. The current study collected a dual-source data set of facial expressions and head poses from an online learning class in a middle school. A deep learning neural network using AlexNet with an attention mechanism was developed to verify the syncretic effect on affective computing of the proposed dual-source fusion strategy. The results show that the dual-source fusion approach significantly outperforms the single-source approach based on the AC recognition accuracy between the two approaches (dual-source approach using Attention-AlexNet model 80.96%; single-source approach, facial expression 76.65% and head pose 64.34%). This study contributes to the theoretical construction of the dual-source data fusion approach, and the empirical validation of the effect of the Attention-AlexNet neural network approach on affective computing in online learning contexts.
{"title":"The Syncretic Effect of Dual-Source Data on Affective Computing in Online Learning Contexts: A Perspective From Convolutional Neural Network With Attention Mechanism","authors":"Xuesong Zhai, Jiaqi Xu, Nian-Shing Chen, Jun Shen, Yan Li, Yonggu Wang, Xiaoyan Chu, Yumeng Zhu","doi":"10.1177/07356331221115663","DOIUrl":"https://doi.org/10.1177/07356331221115663","url":null,"abstract":"Affective computing (AC) has been regarded as a relevant approach to identifying online learners’ mental states and predicting their learning performance. Previous research mainly used one single-source data set, typically learners’ facial expression, to compute learners’ affection. However, a single facial expression may represent different affections in various head poses. This study proposed a dual-source data approach to solve the problem. Facial expression and head pose are two typical data sources that can be captured from online learning videos. The current study collected a dual-source data set of facial expressions and head poses from an online learning class in a middle school. A deep learning neural network using AlexNet with an attention mechanism was developed to verify the syncretic effect on affective computing of the proposed dual-source fusion strategy. The results show that the dual-source fusion approach significantly outperforms the single-source approach based on the AC recognition accuracy between the two approaches (dual-source approach using Attention-AlexNet model 80.96%; single-source approach, facial expression 76.65% and head pose 64.34%). This study contributes to the theoretical construction of the dual-source data fusion approach, and the empirical validation of the effect of the Attention-AlexNet neural network approach on affective computing in online learning contexts.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"61 1","pages":"466 - 493"},"PeriodicalIF":4.8,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43631515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.1177/07356331221121106
M. K. Othman, Syazni Jazlan, Fatin Afiqah Yamin, Shaziti Aman, F. Mohamad, Nurfarahani Norman Anuar, Abdulrazak Yahya Saleh, Ahmad Azaini Abdul Manaf
This study investigates how digital game co-creation promotes Computational Thinking (CT) skills among children in sub-urban primary schools. Understanding how CT skills can be fostered in learning programming concepts through co-creating digital games is crucial to determine instructional strategies that match the young students' interests and capacities. The empirical study has successfully produced a new checklist that can be used as a tool to describe the learning of CT skills when children co-create digital games. The checklist consists of 10 core CT skills: abstraction, decomposition, algorithmic thinking, generalisation, representation, socialisation, code literacy, automation, coordination, and debugging. Thirty-six 10–12 year-olds from sub-urban primary schools in Borneo participated in creating games in three separate eight-hour sessions. In addition, one pilot session with five participants was conducted. The game co-creation process was recorded to identify and determine how these young, inexperienced, untrained young learners collaborated while using CT skills. Analysis of their narratives while co-creating digital games revealed a pattern of using CT while developing the games. Although none of the groups demonstrated the use of all ten CTs, conclusively, all ten components of the CT were visibly present in their co-created digital games.
{"title":"Mapping Computational Thinking Skills Through Digital Games Co-Creation Activity Amongst Malaysian Sub-urban Children","authors":"M. K. Othman, Syazni Jazlan, Fatin Afiqah Yamin, Shaziti Aman, F. Mohamad, Nurfarahani Norman Anuar, Abdulrazak Yahya Saleh, Ahmad Azaini Abdul Manaf","doi":"10.1177/07356331221121106","DOIUrl":"https://doi.org/10.1177/07356331221121106","url":null,"abstract":"This study investigates how digital game co-creation promotes Computational Thinking (CT) skills among children in sub-urban primary schools. Understanding how CT skills can be fostered in learning programming concepts through co-creating digital games is crucial to determine instructional strategies that match the young students' interests and capacities. The empirical study has successfully produced a new checklist that can be used as a tool to describe the learning of CT skills when children co-create digital games. The checklist consists of 10 core CT skills: abstraction, decomposition, algorithmic thinking, generalisation, representation, socialisation, code literacy, automation, coordination, and debugging. Thirty-six 10–12 year-olds from sub-urban primary schools in Borneo participated in creating games in three separate eight-hour sessions. In addition, one pilot session with five participants was conducted. The game co-creation process was recorded to identify and determine how these young, inexperienced, untrained young learners collaborated while using CT skills. Analysis of their narratives while co-creating digital games revealed a pattern of using CT while developing the games. Although none of the groups demonstrated the use of all ten CTs, conclusively, all ten components of the CT were visibly present in their co-created digital games.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"61 1","pages":"355 - 389"},"PeriodicalIF":4.8,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45114071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-28DOI: 10.1177/07356331221133408
Lucía Gorjón, Ainhoa Osés
The increasing presence of technologies at school has triggered a vivid debate on the way ICT influences students’ learning process. Using PISA 2018 data for 15-year-old students and hierarchical linear models, we find an inverted U-shaped relationship between ICT use at school and students’ performance in mathematics in 22 OECD countries. In all cases, the excessive use of technology is associated with a lower academic performance, although this penalty differs across countries, which points to the importance of addressing country-specific analyses. The differentiated profile of those very intensive users, who suffer from above-average bullying exposure, draws into question whether the effect can be deemed as causal. Based on Inverse Probability Weighting techniques, the findings indicate that the very intensive use of ICT at school causes an underperformance of students equivalent to around half an academic course in Estonia, Finland and Spain. The results highlight the need for policy makers and instructors to ensure that the frequent use of ICT at school does not interfere with students’ learning process.
{"title":"The Negative Impact of Information and Communication Technologies Overuse on Student Performance: Evidence From OECD Countries","authors":"Lucía Gorjón, Ainhoa Osés","doi":"10.1177/07356331221133408","DOIUrl":"https://doi.org/10.1177/07356331221133408","url":null,"abstract":"The increasing presence of technologies at school has triggered a vivid debate on the way ICT influences students’ learning process. Using PISA 2018 data for 15-year-old students and hierarchical linear models, we find an inverted U-shaped relationship between ICT use at school and students’ performance in mathematics in 22 OECD countries. In all cases, the excessive use of technology is associated with a lower academic performance, although this penalty differs across countries, which points to the importance of addressing country-specific analyses. The differentiated profile of those very intensive users, who suffer from above-average bullying exposure, draws into question whether the effect can be deemed as causal. Based on Inverse Probability Weighting techniques, the findings indicate that the very intensive use of ICT at school causes an underperformance of students equivalent to around half an academic course in Estonia, Finland and Spain. The results highlight the need for policy makers and instructors to ensure that the frequent use of ICT at school does not interfere with students’ learning process.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"61 1","pages":"723 - 765"},"PeriodicalIF":4.8,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48479453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-27DOI: 10.1177/07356331221132079
Zhen Wang, Yang Cao, Shaoying Gong
Although learner characteristics have been identified as important moderator variables for feedback effectiveness, the question of why learners benefit differently from feedback has only received limited attention. In this study, we investigated: (1) whether learners’ dominant goal orientation moderated the effects of computer-based elaborated feedback on learning; and (2) whether learners’ feedback perception and learning motivation mediated the relationship between elaborated feedback and learning performance. To answer these questions, 101 undergraduates with dominant learning or performance goals were randomly exposed to cue feedback or explanation feedback while working on psychological statistics tasks in a computer-based assessment. Results revealed the moderation role of dominant goal orientation in the relations between elaborated feedback and learning. Specifically, elaborated feedback had more positive effects on dominant learning-oriented learners, but no effects on dominant performance-oriented learners. In addition, feedback perception mediated the moderating effect of dominant goal orientation on the relationship between elaborated feedback and transfer performance. These findings contribute to a better understanding of the role of goal orientations in feedback learning.
{"title":"Who Can Benefit More From More or Less Elaborated Feedback in a Computer-Based Assessment? Dominant Goal Orientation Matters","authors":"Zhen Wang, Yang Cao, Shaoying Gong","doi":"10.1177/07356331221132079","DOIUrl":"https://doi.org/10.1177/07356331221132079","url":null,"abstract":"Although learner characteristics have been identified as important moderator variables for feedback effectiveness, the question of why learners benefit differently from feedback has only received limited attention. In this study, we investigated: (1) whether learners’ dominant goal orientation moderated the effects of computer-based elaborated feedback on learning; and (2) whether learners’ feedback perception and learning motivation mediated the relationship between elaborated feedback and learning performance. To answer these questions, 101 undergraduates with dominant learning or performance goals were randomly exposed to cue feedback or explanation feedback while working on psychological statistics tasks in a computer-based assessment. Results revealed the moderation role of dominant goal orientation in the relations between elaborated feedback and learning. Specifically, elaborated feedback had more positive effects on dominant learning-oriented learners, but no effects on dominant performance-oriented learners. In addition, feedback perception mediated the moderating effect of dominant goal orientation on the relationship between elaborated feedback and transfer performance. These findings contribute to a better understanding of the role of goal orientations in feedback learning.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"61 1","pages":"671 - 695"},"PeriodicalIF":4.8,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47544372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-26DOI: 10.1177/07356331221134423
Ndudi O. Ezeamuzie
Most studies suggest that students develop computational thinking (CT) through learning programming. However, when the target of CT is decoupled from programming, emerging evidence challenges the assertion of CT transferability from programming. In this study, CT was operationalized in everyday problem-solving contexts in a learning experiment (n = 59) that investigated whether learning programming enhances students’ CT skills. Specifically, this study examined the influence of a novel, systematic and micro instructional strategy that is grounded in abstraction and comprised of four independent but related processes – discover, extract, create, and assemble (DECA) towards simplification of problem-solving. Subsidiary questions explored the effects of students’ age, gender, computer proficiency, and prior programming experience on the development of CT. No significant difference was found between the CT skill and programming knowledge of the groups at the posttest. However, within-group paired t-tests showed that the experimental group that integrated DECA had significant improvement in CT but not in the control group across the pretest-posttest axis. Implications of the inconclusive finding about the transfer of programming skills to CT are emphasized and the arguments for disentangling CT from programming are highlighted.
{"title":"Abstractive-Based Programming Approach to Computational Thinking: Discover, Extract, Create, and Assemble","authors":"Ndudi O. Ezeamuzie","doi":"10.1177/07356331221134423","DOIUrl":"https://doi.org/10.1177/07356331221134423","url":null,"abstract":"Most studies suggest that students develop computational thinking (CT) through learning programming. However, when the target of CT is decoupled from programming, emerging evidence challenges the assertion of CT transferability from programming. In this study, CT was operationalized in everyday problem-solving contexts in a learning experiment (n = 59) that investigated whether learning programming enhances students’ CT skills. Specifically, this study examined the influence of a novel, systematic and micro instructional strategy that is grounded in abstraction and comprised of four independent but related processes – discover, extract, create, and assemble (DECA) towards simplification of problem-solving. Subsidiary questions explored the effects of students’ age, gender, computer proficiency, and prior programming experience on the development of CT. No significant difference was found between the CT skill and programming knowledge of the groups at the posttest. However, within-group paired t-tests showed that the experimental group that integrated DECA had significant improvement in CT but not in the control group across the pretest-posttest axis. Implications of the inconclusive finding about the transfer of programming skills to CT are emphasized and the arguments for disentangling CT from programming are highlighted.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"61 1","pages":"605 - 638"},"PeriodicalIF":4.8,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44688776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-26DOI: 10.1177/07356331221129765
Ryusuke Murata, Fumiya Okubo, T. Minematsu, Yuta Taniguchi, Atsushi Shimada
This study helps improve the early prediction of student performance by RNN-FitNets, which applies knowledge distillation (KD) to the time series direction of the recurrent neural network (RNN) model. The RNN-FitNets replaces the teacher model in KD with “an RNN model with a long-term time-series in which the features during the entire course are inputted” and the student model in KD with “an RNN model with a short-term time-series in which only the features during the early stages are inputted.” As a result, the RNN model in the early stage was trained to output the same results as the more accurate RNN model in the later stages. The experiment compared RNN-FitNets with a normal RNN model on a dataset of 296 university students in total. The results showed that RNN-FitNets can improve early prediction. Moreover, the SHAP value was employed to explain the contribution of the input features to the prediction results by RNN-FitNets. It was shown that RNN-FitNets can consider the future effects of the input features from the early stages of the course.
{"title":"Recurrent Neural Network-FitNets: Improving Early Prediction of Student Performanceby Time-Series Knowledge Distillation","authors":"Ryusuke Murata, Fumiya Okubo, T. Minematsu, Yuta Taniguchi, Atsushi Shimada","doi":"10.1177/07356331221129765","DOIUrl":"https://doi.org/10.1177/07356331221129765","url":null,"abstract":"This study helps improve the early prediction of student performance by RNN-FitNets, which applies knowledge distillation (KD) to the time series direction of the recurrent neural network (RNN) model. The RNN-FitNets replaces the teacher model in KD with “an RNN model with a long-term time-series in which the features during the entire course are inputted” and the student model in KD with “an RNN model with a short-term time-series in which only the features during the early stages are inputted.” As a result, the RNN model in the early stage was trained to output the same results as the more accurate RNN model in the later stages. The experiment compared RNN-FitNets with a normal RNN model on a dataset of 296 university students in total. The results showed that RNN-FitNets can improve early prediction. Moreover, the SHAP value was employed to explain the contribution of the input features to the prediction results by RNN-FitNets. It was shown that RNN-FitNets can consider the future effects of the input features from the early stages of the course.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"61 1","pages":"639 - 670"},"PeriodicalIF":4.8,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45352398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1177/07356331221132651
Heidi Meier, M. Lepp
Especially in large courses, feedback is often given only on the final results; less attention is paid to the programming process. Today, however, some programming environments, e.g., Thonny, log activities during programming and have the functionality of replaying the programming process. This information can be used to provide feedback, and this knowledge can be integrated into practical sessions in the classroom. This study aimed to analyse how feedback based on logs affects exam results, task completion time, the number of runs, error messages, and pastes (of the whole group and beginners and non-beginners separately). An experiment was conducted in 2020 and 2021 in the course “Introduction to Programming”. Some groups received additional feedback on homework throughout the course based on log information; the remaining groups worked as usual. Based on the information received from the logs, general recommendations were also offered in the practical sessions. Our study showed that feedback based on logs improved mainly exam test results and programming task solving time among beginners. Therefore, it would be a good method to use, especially in beginner groups.
{"title":"Effectiveness of Feedback Based on Log File Analysis in Introductory Programming Courses","authors":"Heidi Meier, M. Lepp","doi":"10.1177/07356331221132651","DOIUrl":"https://doi.org/10.1177/07356331221132651","url":null,"abstract":"Especially in large courses, feedback is often given only on the final results; less attention is paid to the programming process. Today, however, some programming environments, e.g., Thonny, log activities during programming and have the functionality of replaying the programming process. This information can be used to provide feedback, and this knowledge can be integrated into practical sessions in the classroom. This study aimed to analyse how feedback based on logs affects exam results, task completion time, the number of runs, error messages, and pastes (of the whole group and beginners and non-beginners separately). An experiment was conducted in 2020 and 2021 in the course “Introduction to Programming”. Some groups received additional feedback on homework throughout the course based on log information; the remaining groups worked as usual. Based on the information received from the logs, general recommendations were also offered in the practical sessions. Our study showed that feedback based on logs improved mainly exam test results and programming task solving time among beginners. Therefore, it would be a good method to use, especially in beginner groups.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"39 1","pages":"696 - 719"},"PeriodicalIF":4.8,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65173340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1177/07356331221114183
Li Cheng, Xiaoman Wang, Albert D. Ritzhaupt
Computational thinking is believed to be beneficial for Science, Technology, Engineering, and Mathematics (STEM) learning as it is closely related to many other skills required by STEM disciplines. There has been an increasing interest in integrating computational thinking into STEM and many studies have been conducted to examine the effects of this intervention. This meta-analysis examined the effects of computational thinking integration in STEM on students’ STEM learning performance in the K-12 education context. Following systematic procedures, we identified 20 publications with 21 studies meeting the inclusion and exclusion criteria from a range of academic databases. We extracted effect sizes on student learning outcomes in one-group pretest-posttest designs. We also examined a range of moderating variables in the models, including student levels, STEM disciplines, intervention durations, alignment with content standards (e.g., CSTA/NGSS), types of intervention (e.g., simulation), and the use of unplugged/plugged activities. Overall, we found a statistically significant large effect size (g = 0. 85 [95% CI of 0.57–1.14]; p < .001), indicating a large overall effect of computational thinking integration on STEM learning outcomes. The effect sizes were significantly moderated by intervention durations. We provide a discussion of the findings and present implications for future research and practice.
{"title":"The Effects of Computational Thinking Integration in STEM on Students’ Learning Performance in K-12 Education: A Meta-analysis","authors":"Li Cheng, Xiaoman Wang, Albert D. Ritzhaupt","doi":"10.1177/07356331221114183","DOIUrl":"https://doi.org/10.1177/07356331221114183","url":null,"abstract":"Computational thinking is believed to be beneficial for Science, Technology, Engineering, and Mathematics (STEM) learning as it is closely related to many other skills required by STEM disciplines. There has been an increasing interest in integrating computational thinking into STEM and many studies have been conducted to examine the effects of this intervention. This meta-analysis examined the effects of computational thinking integration in STEM on students’ STEM learning performance in the K-12 education context. Following systematic procedures, we identified 20 publications with 21 studies meeting the inclusion and exclusion criteria from a range of academic databases. We extracted effect sizes on student learning outcomes in one-group pretest-posttest designs. We also examined a range of moderating variables in the models, including student levels, STEM disciplines, intervention durations, alignment with content standards (e.g., CSTA/NGSS), types of intervention (e.g., simulation), and the use of unplugged/plugged activities. Overall, we found a statistically significant large effect size (g = 0. 85 [95% CI of 0.57–1.14]; p < .001), indicating a large overall effect of computational thinking integration on STEM learning outcomes. The effect sizes were significantly moderated by intervention durations. We provide a discussion of the findings and present implications for future research and practice.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"61 1","pages":"416 - 443"},"PeriodicalIF":4.8,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49058866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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