Pub Date : 2021-07-01DOI: 10.1109/ICALT52272.2021.00014
Oscar Karnalim, Simon
Some code similarity detectors are designed to address academic integrity in early programming courses by recognising subtle variations, in the assumption that the code similarity in these courses is typically higher than that in later courses. Although the assumption is often used, it has no empirical evidence, and might be misleading. This study empirically investigates the assumption by examining the relationship between code similarity and course semester in seven programming courses with a total of 931 distinct assessment tasks. Our study shows that the argument is not necessarily true since in later courses, some assessment tasks require the students to follow a particular structure, to use external libraries, or to implement specific algorithms taught during the course.
{"title":"Relationship between Code Similarity and Course Semester in Programming Assessments","authors":"Oscar Karnalim, Simon","doi":"10.1109/ICALT52272.2021.00014","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00014","url":null,"abstract":"Some code similarity detectors are designed to address academic integrity in early programming courses by recognising subtle variations, in the assumption that the code similarity in these courses is typically higher than that in later courses. Although the assumption is often used, it has no empirical evidence, and might be misleading. This study empirically investigates the assumption by examining the relationship between code similarity and course semester in seven programming courses with a total of 931 distinct assessment tasks. Our study shows that the argument is not necessarily true since in later courses, some assessment tasks require the students to follow a particular structure, to use external libraries, or to implement specific algorithms taught during the course.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":"310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116758899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICALT52272.2021.00115
Jim B.J. Huang, Anna Y. Q. Huang, Owen H. T. Lu, Stephen J. H. Yang
Many previous studies have shown that students' learning strategies and engagement are both important factors affecting students’ learning outcome. We want to know whether the engagement of students who use different learning strategies is different, so that we can proxy measure students’ engagement according to the learning strategies used by students. Different from the past methods of using questionnaires or interviews, this research used education data mining technology to directly analyze the learning logs of online learning system to extract students' learning strategies. We collected the learning logs of 56 students, and extracted the learning strategies of the students using sequence clustering. We classified four learning strategies and performed Pearson correlation analysis with students’ learning outcome and engagement, and discussed the key strategies that affect students’ learning outcome. We also found that students who use different learning strategies have different levels of engagement.
{"title":"Exploring Learning Strategies by Sequence Clustering and Analysing their Correlation with Student's Engagement and Learning Outcome","authors":"Jim B.J. Huang, Anna Y. Q. Huang, Owen H. T. Lu, Stephen J. H. Yang","doi":"10.1109/ICALT52272.2021.00115","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00115","url":null,"abstract":"Many previous studies have shown that students' learning strategies and engagement are both important factors affecting students’ learning outcome. We want to know whether the engagement of students who use different learning strategies is different, so that we can proxy measure students’ engagement according to the learning strategies used by students. Different from the past methods of using questionnaires or interviews, this research used education data mining technology to directly analyze the learning logs of online learning system to extract students' learning strategies. We collected the learning logs of 56 students, and extracted the learning strategies of the students using sequence clustering. We classified four learning strategies and performed Pearson correlation analysis with students’ learning outcome and engagement, and discussed the key strategies that affect students’ learning outcome. We also found that students who use different learning strategies have different levels of engagement.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125579141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICALT52272.2021.00140
Hafsa Al Ansari, Rupert R. Ward, Richard Hill
This study investigates enhancing student learning and performance by exploring student motivation through the use of learning analytics. A mixed-methods approach will be used to collect data from Computer Science students within the UK higher education sector. The collected data will be analyzed using thematic analysis to develop a theoretical framework that will be tested subsequently using structural equation modeling. The identification of student motivation factors helps tutors and learning analysts to better understand student learning motivation and adapt their learning practices accordingly.
{"title":"Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK","authors":"Hafsa Al Ansari, Rupert R. Ward, Richard Hill","doi":"10.1109/ICALT52272.2021.00140","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00140","url":null,"abstract":"This study investigates enhancing student learning and performance by exploring student motivation through the use of learning analytics. A mixed-methods approach will be used to collect data from Computer Science students within the UK higher education sector. The collected data will be analyzed using thematic analysis to develop a theoretical framework that will be tested subsequently using structural equation modeling. The identification of student motivation factors helps tutors and learning analysts to better understand student learning motivation and adapt their learning practices accordingly.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124207326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The advancements in learning analytics and artificial intelligence have shown potential to transform traditional modalities of education. One such advancement relates to the use of educational data to track students’ knowledge state [1] . In the field of Artificial Intelligence in Education knowledge tracing is a well-established area where a machine models the students’ knowledge as they interact with coursework. Effective modeling of student knowledge can have a high impact on the provision of adaptive learning. In fact, lately, research on knowledge tracing is intensifying with a particular focus on the utilisation of new machine learning algorithms for modelling the students’ knowledge levels and for the prediction of performance on future tasks and assessment questions [2] . In the case of question-level assessment, knowledge tracing provides an interpretation of the learner’s current knowledge level and models their mastery of the skill or knowledge component to which future questions are related [3] .
{"title":"A step towards Improving Knowledge Tracing","authors":"Aayesha Zia, Jalal Nouri, M. Afzaal, Yongchao Wu, Xiu Li, Rebecka Weegar","doi":"10.1109/ICALT52272.2021.00019","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00019","url":null,"abstract":"The advancements in learning analytics and artificial intelligence have shown potential to transform traditional modalities of education. One such advancement relates to the use of educational data to track students’ knowledge state [1] . In the field of Artificial Intelligence in Education knowledge tracing is a well-established area where a machine models the students’ knowledge as they interact with coursework. Effective modeling of student knowledge can have a high impact on the provision of adaptive learning. In fact, lately, research on knowledge tracing is intensifying with a particular focus on the utilisation of new machine learning algorithms for modelling the students’ knowledge levels and for the prediction of performance on future tasks and assessment questions [2] . In the case of question-level assessment, knowledge tracing provides an interpretation of the learner’s current knowledge level and models their mastery of the skill or knowledge component to which future questions are related [3] .","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126428262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICALT52272.2021.00095
Rwitajit Majumdar, Aditi Kothiyal, Shitanshu Mishra, Prajakt Pande, Huiyong Li, Y. Yang, H. Ogata, J. Warriem
ENaCT is a framework for the design and analysis of critical thinking environments based on 4E cognition perspectives. In this paper, we describe a web-based critical thinking environment designed to implementœ the ENaCT framework. When users perform a critical thinking task in the environment their interaction logs are captured. We report on a pilot study with undergraduate participants and analyse how participants used the affordances in the environment as they performed the critical thinking task. One case of task-related behaviours (high activity) is elaborated to highlight the current possibilities of the system and discuss implications for redesign.
{"title":"Design of a Critical Thinking Task Environment based on ENaCT framework","authors":"Rwitajit Majumdar, Aditi Kothiyal, Shitanshu Mishra, Prajakt Pande, Huiyong Li, Y. Yang, H. Ogata, J. Warriem","doi":"10.1109/ICALT52272.2021.00095","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00095","url":null,"abstract":"ENaCT is a framework for the design and analysis of critical thinking environments based on 4E cognition perspectives. In this paper, we describe a web-based critical thinking environment designed to implementœ the ENaCT framework. When users perform a critical thinking task in the environment their interaction logs are captured. We report on a pilot study with undergraduate participants and analyse how participants used the affordances in the environment as they performed the critical thinking task. One case of task-related behaviours (high activity) is elaborated to highlight the current possibilities of the system and discuss implications for redesign.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124228478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICALT52272.2021.00110
Li En Chen, Shein-Yung Cheng, J. Heh
In the last few years there has been a fast growing up of the use of Chatbots in various fields, such as Customer Service, Marketing, Educational, Health Care and many others. This paper develops a Question Answering System in educational domain. Using Word Embedding, Latent Dirichlet Allocation and Text summarization method build 3 knowledge bases. According question sentence analysis then LineBot retrieve data from these knowledge bases and provides proper answers to the student. Comparison experimental group-used LineBot hits and reading time in e-learning system, are more than control group.
{"title":"Chatbot : A Question Answering System for Student","authors":"Li En Chen, Shein-Yung Cheng, J. Heh","doi":"10.1109/ICALT52272.2021.00110","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00110","url":null,"abstract":"In the last few years there has been a fast growing up of the use of Chatbots in various fields, such as Customer Service, Marketing, Educational, Health Care and many others. This paper develops a Question Answering System in educational domain. Using Word Embedding, Latent Dirichlet Allocation and Text summarization method build 3 knowledge bases. According question sentence analysis then LineBot retrieve data from these knowledge bases and provides proper answers to the student. Comparison experimental group-used LineBot hits and reading time in e-learning system, are more than control group.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115377054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICALT52272.2021.00133
André Helgert, S. Eimler, Alexander Arntz
Street harassment, especially against women, is a prevalent phenomenon also known as catcalling. Numerous campaigns have tried to raise awareness for practices like publicly commenting on women's bodies, whistling or unwanted sexual advances. Recently, activists against catcalling have been especially present on Social Media, outreaching to large international audiences. However, effective ways still have to be found to stimulate an intense reflexion and raise empathy, especially among aggressors and bystanders. Using a virtual reality gallery, including diverse multimedia material and feedback options to stimulate a discussion among visitors, we create an immersive, educating and interactive experience. Rather than moralizing, the gallery promotes a self-paced exploration of the material, combines personal stories with instructive facts to sensitize to street harassment.
{"title":"Stop Catcalling - A Virtual Environment Educating Against Street Harassment","authors":"André Helgert, S. Eimler, Alexander Arntz","doi":"10.1109/ICALT52272.2021.00133","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00133","url":null,"abstract":"Street harassment, especially against women, is a prevalent phenomenon also known as catcalling. Numerous campaigns have tried to raise awareness for practices like publicly commenting on women's bodies, whistling or unwanted sexual advances. Recently, activists against catcalling have been especially present on Social Media, outreaching to large international audiences. However, effective ways still have to be found to stimulate an intense reflexion and raise empathy, especially among aggressors and bystanders. Using a virtual reality gallery, including diverse multimedia material and feedback options to stimulate a discussion among visitors, we create an immersive, educating and interactive experience. Rather than moralizing, the gallery promotes a self-paced exploration of the material, combines personal stories with instructive facts to sensitize to street harassment.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132512890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICALT52272.2021.00075
B. I. Hougaard, H. Knoche, Majken Grünfeld
In light of COVID-19, we created a novel simulation game, to explain exponential growth in disease spread. The simulation game is an open educational ressource (OER) for children to reflect on how test and isolation can be applied to stop contagious diseases. The game was reviewed in three classrooms (P3-P5) by a primary school teacher to pilot the applicability of the game in an educational setting. Based on qualitative feedback from pupils, we developed accompanying exercise sheets and website in close collaboration with the teacher.
{"title":"Pandemic as Game Mechanic: Simulation of Infection Spread for the Classroom.","authors":"B. I. Hougaard, H. Knoche, Majken Grünfeld","doi":"10.1109/ICALT52272.2021.00075","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00075","url":null,"abstract":"In light of COVID-19, we created a novel simulation game, to explain exponential growth in disease spread. The simulation game is an open educational ressource (OER) for children to reflect on how test and isolation can be applied to stop contagious diseases. The game was reviewed in three classrooms (P3-P5) by a primary school teacher to pilot the applicability of the game in an educational setting. Based on qualitative feedback from pupils, we developed accompanying exercise sheets and website in close collaboration with the teacher.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130869515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICALT52272.2021.00130
Yu Liu, Yue Liu, Kang Yue
With the rapid development of mobile learning technologies as well as relevant hardware and software platforms, there is a bright prospect for mixed reality (MR) applying in the field of education. However, appropriate knowledge navigation and concept arrangement methods are urgent to diminish students’ cognitive load in the virtual learning environment. In this preliminary study, concept maps were selected as scaffolding tools to help students navigate through the MR learning space, and an educational game prototype named MMRCM integrating MR and concept maps was developed to investigate students’ technology acceptance about it. Considering the novelty of this new type of MR application, an extended version of the Technology Acceptance Model (TAM) was constructed with the MR game design elements and the concept map usefulness as external factors. A middle school physics experiment using MMRCM was conducted to help students learn the abstract concepts of friction. The evaluation results showed that the model’s external factors have significant correlations with both perceived ease of use and perceived usefulness. The findings indicated explicit intention to use MMRCM, which implied that MR gaming and concept maps could be integrated as an effective instructional tool in science education.
{"title":"Investigating the Factors that Influence Technology Acceptance of an Educational Game Integrating Mixed Reality and Concept Maps","authors":"Yu Liu, Yue Liu, Kang Yue","doi":"10.1109/ICALT52272.2021.00130","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00130","url":null,"abstract":"With the rapid development of mobile learning technologies as well as relevant hardware and software platforms, there is a bright prospect for mixed reality (MR) applying in the field of education. However, appropriate knowledge navigation and concept arrangement methods are urgent to diminish students’ cognitive load in the virtual learning environment. In this preliminary study, concept maps were selected as scaffolding tools to help students navigate through the MR learning space, and an educational game prototype named MMRCM integrating MR and concept maps was developed to investigate students’ technology acceptance about it. Considering the novelty of this new type of MR application, an extended version of the Technology Acceptance Model (TAM) was constructed with the MR game design elements and the concept map usefulness as external factors. A middle school physics experiment using MMRCM was conducted to help students learn the abstract concepts of friction. The evaluation results showed that the model’s external factors have significant correlations with both perceived ease of use and perceived usefulness. The findings indicated explicit intention to use MMRCM, which implied that MR gaming and concept maps could be integrated as an effective instructional tool in science education.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128383204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICALT52272.2021.00108
Danny C.L. Tsai, Anna Y. Q. Huang, Owen H. T. Lu, Stephen J. H. Yang
In recent years, educational resources have gradually been digitized, and digital education platforms have gradually become popular. We use AI to accurately assist people in performing daily tasks through a machine learning process. In education, we can use AI in many situations, such as predicting student's learning outcome and discovering student's learning strategies. However, most solutions have not yet utilized modern AI capabilities, such as natural language processing. This research aims to help teachers use machines to automatically generate short answer questions to reduce the time for teachers to write exam questions. In addition, the main reason we focus on short answers is that many studies prove that short answer exercises can enhance student's long-term memory, thereby improving their learning performance. We propose an automatic question generation (AQG) system that combines syntax-base and semantics-base, in order to prove that the system is highly available and improve student's learning performance, we conducted experiments with 41 students. The experimental results show that student’s learning performance has been significantly improved, which means that by repeatedly testing the machine question generation system, students can deepen their long-term memory of course knowledge.
{"title":"Automatic Question Generation for Repeated Testing to Improve Student Learning Outcome","authors":"Danny C.L. Tsai, Anna Y. Q. Huang, Owen H. T. Lu, Stephen J. H. Yang","doi":"10.1109/ICALT52272.2021.00108","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00108","url":null,"abstract":"In recent years, educational resources have gradually been digitized, and digital education platforms have gradually become popular. We use AI to accurately assist people in performing daily tasks through a machine learning process. In education, we can use AI in many situations, such as predicting student's learning outcome and discovering student's learning strategies. However, most solutions have not yet utilized modern AI capabilities, such as natural language processing. This research aims to help teachers use machines to automatically generate short answer questions to reduce the time for teachers to write exam questions. In addition, the main reason we focus on short answers is that many studies prove that short answer exercises can enhance student's long-term memory, thereby improving their learning performance. We propose an automatic question generation (AQG) system that combines syntax-base and semantics-base, in order to prove that the system is highly available and improve student's learning performance, we conducted experiments with 41 students. The experimental results show that student’s learning performance has been significantly improved, which means that by repeatedly testing the machine question generation system, students can deepen their long-term memory of course knowledge.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130628148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：