Pub Date : 2021-07-01DOI: 10.1109/ICALT52272.2021.00122
Georgia Iatraki, Michael Delimitros, Ioannis Vrellis, T. Mikropoulos
The number of students with Intellectual Disability (ID) enrolled in general classes increases. They appear to meet difficulties in acquiring basic science literacy skills and addressing grade-aligned curriculum. Digital technology seems to contribute to this challenge by engaging students in augmented and virtual environments, especially by enabling 3D representations of abstract and difficult to visualize physical magnitudes and phenomena. This work investigates design issues regarding the development of digital learning environments that contribute to Science Education for students with ID. In this phase, the study explores the perceptions of different groups of professionals and a student with ID regarding the structure of matter. They interacted with different versions of both an augmented and a virtual environment simulating the water vapor in microscopic view. A focus group discussion revealed important data concerning presence, simulator sickness, acceptance and satisfaction for the two environments. The results show that professionals prefer engaging, rather than scientifically aligned representations. The choice between augmented and virtual reality seems to depend on the instructional objectives and strategies based on the specific academic profile of each student with ID.
{"title":"Augmented and virtual environments for students with intellectual disability: design issues in Science Education","authors":"Georgia Iatraki, Michael Delimitros, Ioannis Vrellis, T. Mikropoulos","doi":"10.1109/ICALT52272.2021.00122","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00122","url":null,"abstract":"The number of students with Intellectual Disability (ID) enrolled in general classes increases. They appear to meet difficulties in acquiring basic science literacy skills and addressing grade-aligned curriculum. Digital technology seems to contribute to this challenge by engaging students in augmented and virtual environments, especially by enabling 3D representations of abstract and difficult to visualize physical magnitudes and phenomena. This work investigates design issues regarding the development of digital learning environments that contribute to Science Education for students with ID. In this phase, the study explores the perceptions of different groups of professionals and a student with ID regarding the structure of matter. They interacted with different versions of both an augmented and a virtual environment simulating the water vapor in microscopic view. A focus group discussion revealed important data concerning presence, simulator sickness, acceptance and satisfaction for the two environments. The results show that professionals prefer engaging, rather than scientifically aligned representations. The choice between augmented and virtual reality seems to depend on the instructional objectives and strategies based on the specific academic profile of each student with ID.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132823989","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/ICALT52272.2021.00060
Stanislav Pozdniakov, Roberto Martínez Maldonado, Shaveen Singh, Peter Chen, D. Richardson, Tom Bartindale, P. Olivier, D. Gašević
One of the ultimate goals of several learning analytics (LA) initiatives is to close the loop and support students’ and teachers’ reflective practices. Although there has been a proliferation of end-user interfaces (often in the form of dashboards), various limitations have already been identified in the literature such as little account for sensemaking needs. This paper addresses these limitations by proposing a question-driven LA design approach to ensure that end-user LA interfaces explicitly address teachers’ questions. We illustrate this in the context of synchronous online activities orchestrated by pairs of teachers using audio-visual and text-based tools (Zoom and Google Docs). This led to the design of an open-source monitoring tool to be used in real-time by teachers when students work collaboratively in breakout rooms, and across learning spaces.
{"title":"Question-driven Learning Analytics: Designing a Teacher Dashboard for Online Breakout Rooms","authors":"Stanislav Pozdniakov, Roberto Martínez Maldonado, Shaveen Singh, Peter Chen, D. Richardson, Tom Bartindale, P. Olivier, D. Gašević","doi":"10.1109/ICALT52272.2021.00060","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00060","url":null,"abstract":"One of the ultimate goals of several learning analytics (LA) initiatives is to close the loop and support students’ and teachers’ reflective practices. Although there has been a proliferation of end-user interfaces (often in the form of dashboards), various limitations have already been identified in the literature such as little account for sensemaking needs. This paper addresses these limitations by proposing a question-driven LA design approach to ensure that end-user LA interfaces explicitly address teachers’ questions. We illustrate this in the context of synchronous online activities orchestrated by pairs of teachers using audio-visual and text-based tools (Zoom and Google Docs). This led to the design of an open-source monitoring tool to be used in real-time by teachers when students work collaboratively in breakout rooms, and across learning spaces.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133552948","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/ICALT52272.2021.00102
Theodora Papazoglou, C. Karagiannidis, S. Mavropoulou
The aim of this study was to evaluate the impact of an Educational Robotics intervention using Lego Wedo 2.0® on the social status of students with autism in primary education inclusive contexts. The participants in this study were 14 students with autism and their peers (n=228) from 14 classes in 12 primary schools in Greece. Sociometric methods and tools were used before and after the intervention regarding the students’ social status. The students were categorized according to their social status. The analysis of the research data revealed interesting results concerning the effect of this Educational Robotics intervention on the social status of students with autism in inclusive educational contexts.
{"title":"Educational Robotics can foster social inclusion and social status of children with autism","authors":"Theodora Papazoglou, C. Karagiannidis, S. Mavropoulou","doi":"10.1109/ICALT52272.2021.00102","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00102","url":null,"abstract":"The aim of this study was to evaluate the impact of an Educational Robotics intervention using Lego Wedo 2.0® on the social status of students with autism in primary education inclusive contexts. The participants in this study were 14 students with autism and their peers (n=228) from 14 classes in 12 primary schools in Greece. Sociometric methods and tools were used before and after the intervention regarding the students’ social status. The students were categorized according to their social status. The analysis of the research data revealed interesting results concerning the effect of this Educational Robotics intervention on the social status of students with autism in inclusive educational contexts.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132439179","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/ICALT52272.2021.00045
Xiaoying Qi, Bian Wu
Group knowledge construction is seen as a symbol of effective collaboration. The quality of collaborative knowledge construction can be understood through the extended analysis of discourse. Cohesion is the basis of dialogue discourse, indicating the consistency of contextual topics in conversation. The study adopts natural language processing (NLP) and machine learning approaches based on discourse cohesion metrics to model and visualize the process of group knowledge construction. The three dimensions of cohesion metrics includes internal cohesion, social impact and responsivity. A group conversation dataset (participant N = 3, utterance N = 2,595) in the context of data inquiry learning is used for analyzing individual performance. Combined with the analysis of the actual conversation content, the visualization results show that it can describe the performance of participants in the group knowledge construction effectively. It has great potential to assist instructors to monitor and evaluate each participant’s performance in group discussion efficiently and provide guidance and scaffolds from the perspective of collaboration quality.
{"title":"Modeling and Visualization of Group Knowledge Construction based on Cohesion Metrics in Data Inquiry Learning","authors":"Xiaoying Qi, Bian Wu","doi":"10.1109/ICALT52272.2021.00045","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00045","url":null,"abstract":"Group knowledge construction is seen as a symbol of effective collaboration. The quality of collaborative knowledge construction can be understood through the extended analysis of discourse. Cohesion is the basis of dialogue discourse, indicating the consistency of contextual topics in conversation. The study adopts natural language processing (NLP) and machine learning approaches based on discourse cohesion metrics to model and visualize the process of group knowledge construction. The three dimensions of cohesion metrics includes internal cohesion, social impact and responsivity. A group conversation dataset (participant N = 3, utterance N = 2,595) in the context of data inquiry learning is used for analyzing individual performance. Combined with the analysis of the actual conversation content, the visualization results show that it can describe the performance of participants in the group knowledge construction effectively. It has great potential to assist instructors to monitor and evaluate each participant’s performance in group discussion efficiently and provide guidance and scaffolds from the perspective of collaboration quality.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128832571","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/ICALT52272.2021.00139
Sergio Serrano-Iglesias, E. Gómez-Sánchez, Miguel L. Bote-Lorenzo
This PhD research explores how Smart Learning Environments can support the connection between formal and informal learning. Thanks to the information offered by learning systems and tools such as Virtual Learning Environments, mobile and Internet of Things devices, SLEs can characterize the individual learning needs and context of students to provide them with personalized support across the boundaries of the classroom. In a similar fashion to approaches related with mobile learning, the connection offered by SLEs can help students to reflect on learning concepts in real scenarios, but also adapting the offered resources to their progression and performance throughout the learning situation. However, existing attempts in SLEs face difficulties regarding the preparation of possible interventions by teachers or the understanding of the formal learning situation. This work attempts to overcome this limitations with the usage of the learning design.
{"title":"Connecting formal and informal learning in Smart Learning Environments","authors":"Sergio Serrano-Iglesias, E. Gómez-Sánchez, Miguel L. Bote-Lorenzo","doi":"10.1109/ICALT52272.2021.00139","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00139","url":null,"abstract":"This PhD research explores how Smart Learning Environments can support the connection between formal and informal learning. Thanks to the information offered by learning systems and tools such as Virtual Learning Environments, mobile and Internet of Things devices, SLEs can characterize the individual learning needs and context of students to provide them with personalized support across the boundaries of the classroom. In a similar fashion to approaches related with mobile learning, the connection offered by SLEs can help students to reflect on learning concepts in real scenarios, but also adapting the offered resources to their progression and performance throughout the learning situation. However, existing attempts in SLEs face difficulties regarding the preparation of possible interventions by teachers or the understanding of the formal learning situation. This work attempts to overcome this limitations with the usage of the learning design.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114429517","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/ICALT52272.2021.00063
F. Zhao, Etsuko Kumamoto, Chengjiu Yin
As a kind of data that can reflect learning status, e-book logs have been widely used in learning analytics, especially for the prediction of academic performance. However, the best prediction model cannot be found without determining the contribution of e-book logs to the prediction performance of the model and its creation process. To this end, this study used the scikit-learn, a free software machine learning library, to analyze learning performance of 234 participants by learning behavior logs, which were collected by an e-book system. Finally, six prediction models containing Decision Tree, Random Forests, XGBoost, Logistic Regression, Support Vector Machines, and K-nearest Neighbors were created. Also, the contribution of e-book logs on the establishment of different prediction models was obtained by three feature importance calculation methods, i.e., the impurity-based feature importance, coefficients feature importance, and permutation feature importance. Based on statistical results, it was concluded that the Decision Tree and Random Forests had the best prediction performance, which was compared to the other four models, with prediction performance scores ranging from 0.7 to 0.8. Besides, the four data features of Prev, Highlight, Maker, and Next were found to have the greatest impact on model prediction creation.
{"title":"The effect and contribution of e-book logs to model creation for predicting students’ academic performance","authors":"F. Zhao, Etsuko Kumamoto, Chengjiu Yin","doi":"10.1109/ICALT52272.2021.00063","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00063","url":null,"abstract":"As a kind of data that can reflect learning status, e-book logs have been widely used in learning analytics, especially for the prediction of academic performance. However, the best prediction model cannot be found without determining the contribution of e-book logs to the prediction performance of the model and its creation process. To this end, this study used the scikit-learn, a free software machine learning library, to analyze learning performance of 234 participants by learning behavior logs, which were collected by an e-book system. Finally, six prediction models containing Decision Tree, Random Forests, XGBoost, Logistic Regression, Support Vector Machines, and K-nearest Neighbors were created. Also, the contribution of e-book logs on the establishment of different prediction models was obtained by three feature importance calculation methods, i.e., the impurity-based feature importance, coefficients feature importance, and permutation feature importance. Based on statistical results, it was concluded that the Decision Tree and Random Forests had the best prediction performance, which was compared to the other four models, with prediction performance scores ranging from 0.7 to 0.8. Besides, the four data features of Prev, Highlight, Maker, and Next were found to have the greatest impact on model prediction creation.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116486011","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/ICALT52272.2021.00135
Tao Wu, Maiga Chang
This study investigates the mutual influence among the different components of learning motivation, self-regulation, and Gender as well as their effect on the academic performance. The data is collected from 301 undergraduates enrolled accounting major. The results indicate that test anxiety has negative influence on academic performance and the use of cognitive strategies is a mediating factor between motivation and self-regulation strategies. Gender difference does exist in students' test anxiety but there is no evidence showing that this gender difference causes any differences in their academic achievements.
{"title":"The Impact and Gender Difference of Learning Motivation and Self-Regulation on Academic Performance in Online Learning Environment","authors":"Tao Wu, Maiga Chang","doi":"10.1109/ICALT52272.2021.00135","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00135","url":null,"abstract":"This study investigates the mutual influence among the different components of learning motivation, self-regulation, and Gender as well as their effect on the academic performance. The data is collected from 301 undergraduates enrolled accounting major. The results indicate that test anxiety has negative influence on academic performance and the use of cognitive strategies is a mediating factor between motivation and self-regulation strategies. Gender difference does exist in students' test anxiety but there is no evidence showing that this gender difference causes any differences in their academic achievements.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120817605","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/ICALT52272.2021.00119
B. Flanagan, Changhao Liang, Rwitajit Majumdar, H. Ogata
In recent years, machine learning of increasing complexity is being applied to problems in education. However, there is an increasing call for transparency and understanding into how the results of complex models are derived, leading to explainable AI gaining attention. The application of machine learning to automated group formation for collaborative work from learning system logs and other data has been progressing. Building on previous research in this field, we propose a group formation method that is based on a combination of course knowledge structures, reading behavior, and assessment analysis to create optimal heterogenous and homogeneous working groups using a genetic algorithm. The characteristics of each group are presented for explanation as a visualized knowledge map showing the strengths and weaknesses of each group, and are in the structure form of curriculum. We also present a case study of applying the method to junior high school mathematics log data, and provide explanation in a visualized form of standardized curriculum of group characteristics that are often referenced for learning design by teachers.
{"title":"Towards Explainable Group Formation by Knowledge Map based Genetic Algorithm","authors":"B. Flanagan, Changhao Liang, Rwitajit Majumdar, H. Ogata","doi":"10.1109/ICALT52272.2021.00119","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00119","url":null,"abstract":"In recent years, machine learning of increasing complexity is being applied to problems in education. However, there is an increasing call for transparency and understanding into how the results of complex models are derived, leading to explainable AI gaining attention. The application of machine learning to automated group formation for collaborative work from learning system logs and other data has been progressing. Building on previous research in this field, we propose a group formation method that is based on a combination of course knowledge structures, reading behavior, and assessment analysis to create optimal heterogenous and homogeneous working groups using a genetic algorithm. The characteristics of each group are presented for explanation as a visualized knowledge map showing the strengths and weaknesses of each group, and are in the structure form of curriculum. We also present a case study of applying the method to junior high school mathematics log data, and provide explanation in a visualized form of standardized curriculum of group characteristics that are often referenced for learning design by teachers.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125855600","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/ICALT52272.2021.00082
Kensuke Takii, B. Flanagan, H. Ogata
Learning vocabulary is a crucial but challenging activity for English as a foreign language learners, and computer-assisted language learning can facilitate this process. Moreover, e-learning is attracting a great deal of attention as a new technology to bring educational support which traditional learning systems cannot provide. Recommender systems as its implementation have been subject to discussion. In this study, we propose a comprehensive learning analytics-based platform for efficient vocabulary learning, including an e-book reader and a book/quiz recommender. The system on this platform estimates learners’ knowledge based on their activities and brings personalized recommendation and its bases to the learners. Also, this platform provides teachers with visualized feedback regarding the recommendation and students’ engagement in learning.
{"title":"EFL Vocabulary Learning Using a Learning Analytics-based E-book and Recommender Platform","authors":"Kensuke Takii, B. Flanagan, H. Ogata","doi":"10.1109/ICALT52272.2021.00082","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00082","url":null,"abstract":"Learning vocabulary is a crucial but challenging activity for English as a foreign language learners, and computer-assisted language learning can facilitate this process. Moreover, e-learning is attracting a great deal of attention as a new technology to bring educational support which traditional learning systems cannot provide. Recommender systems as its implementation have been subject to discussion. In this study, we propose a comprehensive learning analytics-based platform for efficient vocabulary learning, including an e-book reader and a book/quiz recommender. The system on this platform estimates learners’ knowledge based on their activities and brings personalized recommendation and its bases to the learners. Also, this platform provides teachers with visualized feedback regarding the recommendation and students’ engagement in learning.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128213760","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}
This paper presents a method of exercises recommendation based on machine learning. This method can recommend more suitable exercises to students according to the category they belong to. Firstly, we use linear regression and EM algorithm to accurately model the students' mastery of each knowledge point. For each knowledge point, students are divided into several categories according to their mastery of the knowledge point and their average mastery of all knowledge points. For each knowledge point, according to the student history answer record, find out the exercise that can make each kind of student get bigger promotion respectively. For the students who need to recommend the exercises that contain the specified knowledge points, we first use the k-nearest neighbor algorithm to classify the students, and then recommend the exercises suitable for the students. It has been proved by experiments that this method can help students to achieve greater improvement in the same number of exercises.
{"title":"Exercise Recommendation Method Based on Machine Learning","authors":"Zhizhuang Li, Haiyang Hu, Zhipeng Xia, Jianping Zhang, Xiaoli Li, Zisihan Wang, Xiaoke Huang, Shan Zeng, Beixu Qiu","doi":"10.1109/ICALT52272.2021.00023","DOIUrl":"https://doi.org/10.1109/ICALT52272.2021.00023","url":null,"abstract":"This paper presents a method of exercises recommendation based on machine learning. This method can recommend more suitable exercises to students according to the category they belong to. Firstly, we use linear regression and EM algorithm to accurately model the students' mastery of each knowledge point. For each knowledge point, students are divided into several categories according to their mastery of the knowledge point and their average mastery of all knowledge points. For each knowledge point, according to the student history answer record, find out the exercise that can make each kind of student get bigger promotion respectively. For the students who need to recommend the exercises that contain the specified knowledge points, we first use the k-nearest neighbor algorithm to classify the students, and then recommend the exercises suitable for the students. It has been proved by experiments that this method can help students to achieve greater improvement in the same number of exercises.","PeriodicalId":170895,"journal":{"name":"2021 International Conference on Advanced Learning Technologies (ICALT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132407547","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}
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