Pub Date : 2024-04-23DOI: 10.3991/ijim.v18i08.48875
Jie Li, Qian Li
With the continuous advancement of educational technology, interactive mobile and adaptive learning platforms are playing an increasingly important role in the field of education. This is particularly evident in the development of educational design skills among education students, as these platforms showcase their unique value. Educational design capability is a crucial skill for education students, directly related to the quality of designing and implementing future teaching activities. Traditional methods of education often fail to fully consider individual student differences, resulting in inadequate cultivation of personalized capabilities. This study aims to explore and achieve precise cultivation of educational design capabilities in education students through interactive, mobile, and adaptive learning platforms. This paper first reviews the current application of interactive mobile and adaptive learning platforms to cultivate educational design capabilities. It highlights deficiencies in existing research methods related to personalized matching and recommendation system design. To address these deficiencies, this study proposes a new set of adaptive matching methods. These methods include capability characteristic recognition based on competitive advantage thinking, the construction of individual strength models, as well as matching calculation, and decision- making scheme optimization using the projection decision method and the Hungarian method. Additionally, the study designs a learning project recommendation algorithm based on explicit ratings to enhance the accuracy and personalization of learning project recommendations. The application of these methods not only enhances the educational design capabilities of education students but also provides new theoretical support and practical guidance for the development of interactive, mobile, and adaptive learning platforms.
{"title":"Enhancing Educational Design Capabilities through Interactive Mobile and Adaptive Learning Platforms: An Empirical Study","authors":"Jie Li, Qian Li","doi":"10.3991/ijim.v18i08.48875","DOIUrl":"https://doi.org/10.3991/ijim.v18i08.48875","url":null,"abstract":"With the continuous advancement of educational technology, interactive mobile and adaptive learning platforms are playing an increasingly important role in the field of education. This is particularly evident in the development of educational design skills among education students, as these platforms showcase their unique value. Educational design capability is a crucial skill for education students, directly related to the quality of designing and implementing future teaching activities. Traditional methods of education often fail to fully consider individual student differences, resulting in inadequate cultivation of personalized capabilities. This study aims to explore and achieve precise cultivation of educational design capabilities in education students through interactive, mobile, and adaptive learning platforms. This paper first reviews the current application of interactive mobile and adaptive learning platforms to cultivate educational design capabilities. It highlights deficiencies in existing research methods related to personalized matching and recommendation system design. To address these deficiencies, this study proposes a new set of adaptive matching methods. These methods include capability characteristic recognition based on competitive advantage thinking, the construction of individual strength models, as well as matching calculation, and decision- making scheme optimization using the projection decision method and the Hungarian method. Additionally, the study designs a learning project recommendation algorithm based on explicit ratings to enhance the accuracy and personalization of learning project recommendations. The application of these methods not only enhances the educational design capabilities of education students but also provides new theoretical support and practical guidance for the development of interactive, mobile, and adaptive learning platforms.","PeriodicalId":507995,"journal":{"name":"International Journal of Interactive Mobile Technologies (iJIM)","volume":"49 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140667290","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 : 2024-04-23DOI: 10.3991/ijim.v18i08.48455
N. Y. Indriyanti, Khairunnisa Febryana, Bayu Antrakusuma
Learning in the 21st century requires students to have problem-solving and critical thinking skills. In problem-solving related to the environment, students need a strong foundation in environmental literacy. The environmental literacy of eighth-grade students still falls relatively low within the unfavorable category. It was found that several students still needed to increase their awareness of protecting the environment. Students still need assistance in learning about climate change because there are several abstract concepts to grasp. The characteristics of Generation Z include a low attention span and a dislike for lengthy explanations. The purposes of this study were to (1) determine the characteristics of Generation Z, (2) assess the feasibility of utilizing Android-based video series as a learning medium, and (3) analyze the response of Generation Z to such educational content. This research and development method refers to the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. The outcome of this research and development is a video series focusing on climate change, presented in the form of an android. Observation, interviews, and questionnaires are data collection techniques. Learning media validation was conducted by two media experts: a subject matter expert and a language expert. The validity test results were calculated using Aiken’s V index formula. The results obtained for the three experts were 0.92, 0.93, and 0.90. The results of the analysis of teacher and student response tests form the expected characteristics of the video series, receiving very positive feedback.
21 世纪的学习要求学生具备解决问题和批判性思维的能力。在解决与环境有关的问题时，学生需要打下坚实的环境素养基础。八年级学生的环境素养仍属于相对较低的不利类别。调查发现，一些学生仍需增强保护环境的意识。学生在学习气候变化方面仍需要帮助，因为有几个抽象的概念需要掌握。Z 世代的特点包括注意力不集中和不喜欢冗长的解释。本研究的目的是：(1) 确定 Z 世代的特点；(2) 评估利用基于 Android 的系列视频作为学习媒介的可行性；(3) 分析 Z 世代对此类教育内容的反应。这种研发方法参考了 ADDIE 模型，其中包括分析、设计、开发、实施和评估等阶段。本次研发的成果是一个以气候变化为主题的系列视频，以安卓的形式呈现。观察、访谈和问卷调查是数据收集技术。学习媒体验证由两位媒体专家进行：一位是主题专家，另一位是语言专家。有效性测试结果使用艾肯 V 指数公式计算。三位专家的结果分别为 0.92、0.93 和 0.90。教师和学生反应测试的分析结果形成了系列视频的预期特点，得到了非常积极的反馈。
{"title":"Development of Android-Based Video Series on Climate Change Topic to Empower Students’ Environmental Literacy","authors":"N. Y. Indriyanti, Khairunnisa Febryana, Bayu Antrakusuma","doi":"10.3991/ijim.v18i08.48455","DOIUrl":"https://doi.org/10.3991/ijim.v18i08.48455","url":null,"abstract":"Learning in the 21st century requires students to have problem-solving and critical thinking skills. In problem-solving related to the environment, students need a strong foundation in environmental literacy. The environmental literacy of eighth-grade students still falls relatively low within the unfavorable category. It was found that several students still needed to increase their awareness of protecting the environment. Students still need assistance in learning about climate change because there are several abstract concepts to grasp. The characteristics of Generation Z include a low attention span and a dislike for lengthy explanations. The purposes of this study were to (1) determine the characteristics of Generation Z, (2) assess the feasibility of utilizing Android-based video series as a learning medium, and (3) analyze the response of Generation Z to such educational content. This research and development method refers to the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. The outcome of this research and development is a video series focusing on climate change, presented in the form of an android. Observation, interviews, and questionnaires are data collection techniques. Learning media validation was conducted by two media experts: a subject matter expert and a language expert. The validity test results were calculated using Aiken’s V index formula. The results obtained for the three experts were 0.92, 0.93, and 0.90. The results of the analysis of teacher and student response tests form the expected characteristics of the video series, receiving very positive feedback.","PeriodicalId":507995,"journal":{"name":"International Journal of Interactive Mobile Technologies (iJIM)","volume":"49 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140667291","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 : 2024-04-23DOI: 10.3991/ijim.v18i08.46247
A. Quttoum, A. Alsarhan, AbiAlrahman Moh'd, Mohammad Aljaidi, Gassan Samarah, Muteb Alshammari
Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant; however, this trend appears to be changing sooner than we might expect. Countries in Europe, Asia, and many states in America have already made the decision to transition to a fully EV industry in the next few years. This looks promising; however, drivers still have concerns about the battery mileage of such vehicles and the anxiety that such driving experiences! Indeed, driving with the probability of having insufficient battery charge that may be involved in guaranteeing the delivery to the trip destination imposes a level of anxiety on the vehicle drivers. Therefore, for an alternative to traditional fuel-powered vehicles to be convincing, there needs to be sufficient coverage of charging stations to serve cities in the same way that fuel stations serve traditional vehicles. The current navigation models select routes based solely on distance and traffic metrics, without taking into account the coverage of fuel service stations that these routes may offer. This assumption is made under the belief that all routes are adequately covered. This might be true for fuel-powered vehicles, but not for EVs. Hence, in this work, we are presenting AFARM, a routing model that enables a smart navigation system specifically designed for EVs. This model routes the EVs via paths that are lined with charging stations that align with the EV’s current charge requirements. Different from the other models proposed in the literature, AFARM is autonomous in the sense that it determines navigation paths for each vehicle based on its make, model, and current battery status. Moreover, it employs Dijkstra’s algorithm to accommodate varying least-cost navigation preferences, ranging from shortest-distance routes to routes with the shortest trip time and routes with maximum residual battery capacities as well. According to the EV driver’s preference, AFARM checks the set of candidate paths at the source point and selects the appropriate path for the vehicle to drive based on its current status. Consequently, AFARM provides an anxiety-free navigation model that allows for a reliable and environmentally friendly driving experience, promoting this alternative mode of transportation.
{"title":"AFARM: Anxiety-Free Autonomous Routing Model for Electric Vehicles with Dynamic Route Preferences","authors":"A. Quttoum, A. Alsarhan, AbiAlrahman Moh'd, Mohammad Aljaidi, Gassan Samarah, Muteb Alshammari","doi":"10.3991/ijim.v18i08.46247","DOIUrl":"https://doi.org/10.3991/ijim.v18i08.46247","url":null,"abstract":"Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant; however, this trend appears to be changing sooner than we might expect. Countries in Europe, Asia, and many states in America have already made the decision to transition to a fully EV industry in the next few years. This looks promising; however, drivers still have concerns about the battery mileage of such vehicles and the anxiety that such driving experiences! Indeed, driving with the probability of having insufficient battery charge that may be involved in guaranteeing the delivery to the trip destination imposes a level of anxiety on the vehicle drivers. Therefore, for an alternative to traditional fuel-powered vehicles to be convincing, there needs to be sufficient coverage of charging stations to serve cities in the same way that fuel stations serve traditional vehicles. The current navigation models select routes based solely on distance and traffic metrics, without taking into account the coverage of fuel service stations that these routes may offer. This assumption is made under the belief that all routes are adequately covered. This might be true for fuel-powered vehicles, but not for EVs. Hence, in this work, we are presenting AFARM, a routing model that enables a smart navigation system specifically designed for EVs. This model routes the EVs via paths that are lined with charging stations that align with the EV’s current charge requirements. Different from the other models proposed in the literature, AFARM is autonomous in the sense that it determines navigation paths for each vehicle based on its make, model, and current battery status. Moreover, it employs Dijkstra’s algorithm to accommodate varying least-cost navigation preferences, ranging from shortest-distance routes to routes with the shortest trip time and routes with maximum residual battery capacities as well. According to the EV driver’s preference, AFARM checks the set of candidate paths at the source point and selects the appropriate path for the vehicle to drive based on its current status. Consequently, AFARM provides an anxiety-free navigation model that allows for a reliable and environmentally friendly driving experience, promoting this alternative mode of transportation.","PeriodicalId":507995,"journal":{"name":"International Journal of Interactive Mobile Technologies (iJIM)","volume":"118 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140669588","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}
In recent years, there has been a rapid growth of online learning in higher education. Apart from professional online course platforms, many online video sharing websites have also provided online learning opportunities for college students. One of the most popular websites among college students in China is Bilibili, a Shanghai-based Chinese video sharing website known for its danmaku commenting system. This system enables users to post scrolling comments synchronized with the video timeline while the video is playing. Which attracts young students due to the lively user interaction. As a result, an increasing number of Chinese students are utilizing online courses on Bilibili as a supplementary learning resource alongside traditional classroom learning. Despite its popularity, online learning faces the challenge of students’ lack of participation more than traditional face-to-face learning does. To understand their learning involvement, we propose a novel danmaku-based automatic analysis model that extracts three dimensions of online learning engagement using the Text Mind software. This model enables us to understand the students’ learning patterns both as clusters and as individuals. Based on the model results, we present corresponding intervention strategies for different types of students based on their individual engagement characteristics.
近年来，高等教育中的在线学习发展迅速。除了专业的在线课程平台，许多在线视频分享网站也为大学生提供了在线学习的机会。在中国，最受大学生欢迎的网站之一是 Bilibili，这是一家位于上海的中文视频共享网站，以其 "段子手 "评论系统而闻名。该系统使用户能够在视频播放的同时发表与视频时间轴同步的滚动评论。这种生动的用户互动吸引了众多年轻学生。因此，越来越多的中国学生将 Bilibili 上的在线课程作为传统课堂学习的补充学习资源。尽管在线学习很受欢迎，但与传统的面对面学习相比，它面临着学生参与度不足的挑战。为了了解学生的学习参与度，我们提出了一种基于丹幕的新型自动分析模型，利用 Text Mind 软件提取在线学习参与度的三个维度。通过该模型，我们可以了解学生作为群组和个体的学习模式。基于模型结果，我们根据不同类型学生的个体参与特征，提出了相应的干预策略。
{"title":"Danmaku-Based Automatic Analysis of Real-Time Online Learning Engagement","authors":"Linzhou Zeng, Zhibang Tan, Yougang Ke, Lingling Xia","doi":"10.3991/ijim.v18i08.48025","DOIUrl":"https://doi.org/10.3991/ijim.v18i08.48025","url":null,"abstract":"In recent years, there has been a rapid growth of online learning in higher education. Apart from professional online course platforms, many online video sharing websites have also provided online learning opportunities for college students. One of the most popular websites among college students in China is Bilibili, a Shanghai-based Chinese video sharing website known for its danmaku commenting system. This system enables users to post scrolling comments synchronized with the video timeline while the video is playing. Which attracts young students due to the lively user interaction. As a result, an increasing number of Chinese students are utilizing online courses on Bilibili as a supplementary learning resource alongside traditional classroom learning. Despite its popularity, online learning faces the challenge of students’ lack of participation more than traditional face-to-face learning does. To understand their learning involvement, we propose a novel danmaku-based automatic analysis model that extracts three dimensions of online learning engagement using the Text Mind software. This model enables us to understand the students’ learning patterns both as clusters and as individuals. Based on the model results, we present corresponding intervention strategies for different types of students based on their individual engagement characteristics.","PeriodicalId":507995,"journal":{"name":"International Journal of Interactive Mobile Technologies (iJIM)","volume":"34 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140667857","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 : 2024-04-09DOI: 10.3991/ijim.v18i07.48599
Aruna Tao
The rapid advancement of information technology has redefined distance education as a fundamental component of modern education systems. The extensive deployment of online learning platforms has further propelled this transformation, catalyzing innovation in educational methodologies while simultaneously presenting novel challenges and demands in the realm of community engagement. In the sphere of online learning, considerable research has been conducted on the efficacy of these platforms. However, studies specifically dedicated to the effective cultivation and maintenance of community engagement through these platforms are notably scarce. Recognized as pivotal for educational outcomes, student satisfaction, and enduring academic success, community engagement within the context of distance education warrants comprehensive exploration. This study delves into this exploration by developing a dynamic model of interaction and a coupled network evolutionary game model that incorporates the nuances of social group dynamics. Initiating with a critical review of existing literature on community engagement in distance learning, the study identifies prevalent limitations. These limitations include an over-reliance on qualitative data, the absence of dynamic analyses, and an oversight of the intricacies of group interactions. To bridge these gaps, we propose a data-driven interaction dynamics model tailored for online learning platforms. Additionally, we suggest a network evolutionary game model that considers the interplay among social groups. These models collectively deepen our understanding of the evolution of community engagement over time and elucidate how both individual and collective behaviors influence the communal health of online learning environments.
{"title":"The Dynamics of Community Engagement in Distance Education: A Sociological Analysis Based on Online Learning Platforms","authors":"Aruna Tao","doi":"10.3991/ijim.v18i07.48599","DOIUrl":"https://doi.org/10.3991/ijim.v18i07.48599","url":null,"abstract":"The rapid advancement of information technology has redefined distance education as a fundamental component of modern education systems. The extensive deployment of online learning platforms has further propelled this transformation, catalyzing innovation in educational methodologies while simultaneously presenting novel challenges and demands in the realm of community engagement. In the sphere of online learning, considerable research has been conducted on the efficacy of these platforms. However, studies specifically dedicated to the effective cultivation and maintenance of community engagement through these platforms are notably scarce. Recognized as pivotal for educational outcomes, student satisfaction, and enduring academic success, community engagement within the context of distance education warrants comprehensive exploration. This study delves into this exploration by developing a dynamic model of interaction and a coupled network evolutionary game model that incorporates the nuances of social group dynamics. Initiating with a critical review of existing literature on community engagement in distance learning, the study identifies prevalent limitations. These limitations include an over-reliance on qualitative data, the absence of dynamic analyses, and an oversight of the intricacies of group interactions. To bridge these gaps, we propose a data-driven interaction dynamics model tailored for online learning platforms. Additionally, we suggest a network evolutionary game model that considers the interplay among social groups. These models collectively deepen our understanding of the evolution of community engagement over time and elucidate how both individual and collective behaviors influence the communal health of online learning environments.","PeriodicalId":507995,"journal":{"name":"International Journal of Interactive Mobile Technologies (iJIM)","volume":"59 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140721742","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 : 2024-04-09DOI: 10.3991/ijim.v18i07.42261
Piyanoot Wongklang, Jirasak Wipatsopakron
This study aims to develop a problem-based mobile augmented reality (AR) application to enhance creative problem-solving skills among undergraduate students. The research involves an experiment conducted on a sample group of 30 undergraduate students enrolled in the maintenance of computers and audio-visual equipment course, selected through a simple random sampling method. The instruments used in the study included structured interview questions, needs assessment questionnaires, quality evaluation of mobile applications, evaluation of the learning plan, measurement of creative problem-solving ability, and satisfaction surveys. The data obtained were analyzed using mean, standard deviation, dependent t-test, and effect size statistics. The findings demonstrated that the developed mobile application achieved the highest quality level, with an average value of 4.62 (SD = 0.64). The mobile application efficiency reached 75.48 out of 75.16, meeting the established threshold of 75 out of 75. Using the mobile application led to statistically significant improvements in creative problem-solving skills after the learning process, with scores higher than those before learning at the 0.05 level. The effect size was 6.61, indicating a large impact. Additionally, student satisfaction with the mobile application was reported as the highest.
{"title":"Development of Problem-Based Mobile Augmented Reality Application to Enhance Creative Problem-Solving Skills for Undergraduate Students","authors":"Piyanoot Wongklang, Jirasak Wipatsopakron","doi":"10.3991/ijim.v18i07.42261","DOIUrl":"https://doi.org/10.3991/ijim.v18i07.42261","url":null,"abstract":"This study aims to develop a problem-based mobile augmented reality (AR) application to enhance creative problem-solving skills among undergraduate students. The research involves an experiment conducted on a sample group of 30 undergraduate students enrolled in the maintenance of computers and audio-visual equipment course, selected through a simple random sampling method. The instruments used in the study included structured interview questions, needs assessment questionnaires, quality evaluation of mobile applications, evaluation of the learning plan, measurement of creative problem-solving ability, and satisfaction surveys. The data obtained were analyzed using mean, standard deviation, dependent t-test, and effect size statistics. The findings demonstrated that the developed mobile application achieved the highest quality level, with an average value of 4.62 (SD = 0.64). The mobile application efficiency reached 75.48 out of 75.16, meeting the established threshold of 75 out of 75. Using the mobile application led to statistically significant improvements in creative problem-solving skills after the learning process, with scores higher than those before learning at the 0.05 level. The effect size was 6.61, indicating a large impact. Additionally, student satisfaction with the mobile application was reported as the highest.","PeriodicalId":507995,"journal":{"name":"International Journal of Interactive Mobile Technologies (iJIM)","volume":"58 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140723732","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 : 2024-04-09DOI: 10.3991/ijim.v18i07.48313
Lei Huang, Li Ma
At present, most online education platforms still have problems such as single learning modes and loose knowledge structures. Using the knowledge map, the study employs a personalized adaptive intelligent adjustment strategy based on the structural expression of the CBEC English subject system. Firstly, the study uses the Scapy framework to crawl the subject knowledge data. Then use the LTP platform to process sentences containing multiple entities. Input the sentence into the dependency parser to analyze and extract the entity relationship. Finally, according to the relevance between knowledge points and topics in the knowledge map, the final learning path recommendation result is obtained. And cluster the similarity of curriculum content to build a complete curriculum system. Based on the above operations, a knowledge map-based smart learning platform for the CBEC English discipline has been designed and implemented to provide a smart, personalized learning environment for learners. According to the experimental analysis, the average satisfaction of learners with the learning platform designed by the study is 81.56%, which can meet the learning needs of learners and provide an excellent mobile learning environment for students.
{"title":"Design and Implementation of an Intelligent Cross-Border E-Commerce English Learning Platform Based on a Knowledge Map","authors":"Lei Huang, Li Ma","doi":"10.3991/ijim.v18i07.48313","DOIUrl":"https://doi.org/10.3991/ijim.v18i07.48313","url":null,"abstract":"At present, most online education platforms still have problems such as single learning modes and loose knowledge structures. Using the knowledge map, the study employs a personalized adaptive intelligent adjustment strategy based on the structural expression of the CBEC English subject system. Firstly, the study uses the Scapy framework to crawl the subject knowledge data. Then use the LTP platform to process sentences containing multiple entities. Input the sentence into the dependency parser to analyze and extract the entity relationship. Finally, according to the relevance between knowledge points and topics in the knowledge map, the final learning path recommendation result is obtained. And cluster the similarity of curriculum content to build a complete curriculum system. Based on the above operations, a knowledge map-based smart learning platform for the CBEC English discipline has been designed and implemented to provide a smart, personalized learning environment for learners. According to the experimental analysis, the average satisfaction of learners with the learning platform designed by the study is 81.56%, which can meet the learning needs of learners and provide an excellent mobile learning environment for students.","PeriodicalId":507995,"journal":{"name":"International Journal of Interactive Mobile Technologies (iJIM)","volume":"117 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140724318","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 : 2024-04-09DOI: 10.3991/ijim.v18i07.48305
Qing Gao
With the rapid development of computer technology, a new educational model, the innovative education learner model, has emerged as a product of the deep integration of technology and education. In this paper, we will begin by organizing the theories and models related to technology acceptance. We will select the UTAUT model, known for its high explanatory power, as the theoretical framework. Subsequently, we will comprehensively analyze the dataset and conduct in-depth habit mining. The effectiveness of applying K-means concepts to address the classification of clusters of learners’ learning habits is confirmed. The feasibility of the LSTM algorithm in predicting learners for exercise responses is also demonstrated. Next, a learning cluster construction method based on intelligent learner clustering is proposed. The methods of MDS+K-means and spectral clustering are selected for clustering. Learning clusters are constructed, and the performance of the two types of algorithms is compared and analyzed. Finally, the enhanced text feature extraction algorithm is utilized to design and implement the corresponding system for the practical application of the innovative educational learner model. The final experiment proves that the text features extracted by the model are effective, with an error rate of only about 2.8%, thus demonstrating that the intelligent educational learning model in this paper is reasonable.
{"title":"Research on the Construction and Application of an Intelligent Education Learner Model Based on the UTAUT Theory","authors":"Qing Gao","doi":"10.3991/ijim.v18i07.48305","DOIUrl":"https://doi.org/10.3991/ijim.v18i07.48305","url":null,"abstract":"With the rapid development of computer technology, a new educational model, the innovative education learner model, has emerged as a product of the deep integration of technology and education. In this paper, we will begin by organizing the theories and models related to technology acceptance. We will select the UTAUT model, known for its high explanatory power, as the theoretical framework. Subsequently, we will comprehensively analyze the dataset and conduct in-depth habit mining. The effectiveness of applying K-means concepts to address the classification of clusters of learners’ learning habits is confirmed. The feasibility of the LSTM algorithm in predicting learners for exercise responses is also demonstrated. Next, a learning cluster construction method based on intelligent learner clustering is proposed. The methods of MDS+K-means and spectral clustering are selected for clustering. Learning clusters are constructed, and the performance of the two types of algorithms is compared and analyzed. Finally, the enhanced text feature extraction algorithm is utilized to design and implement the corresponding system for the practical application of the innovative educational learner model. The final experiment proves that the text features extracted by the model are effective, with an error rate of only about 2.8%, thus demonstrating that the intelligent educational learning model in this paper is reasonable.","PeriodicalId":507995,"journal":{"name":"International Journal of Interactive Mobile Technologies (iJIM)","volume":"96 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140723132","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 : 2024-04-09DOI: 10.3991/ijim.v18i07.48067
Yudhi Hanggara, Abdul Qohar, Sukoriyanto
The significance of critical thinking skills among students lies in their ability to actively assess, evaluate, and respond thoughtfully to information. This study investigates the impact of a game-based learning (GBL) model that utilizes augmented reality (AR) in mathematics learning games on the CT skills of eighth-grade junior high school students. The study specifically focuses on polyhedrons in mathematics. A quasi-experimental design was utilized to accomplish the study objectives. This study involved 77 students, divided into an experimental group of 40 students and a control group of 37 students. The research instrument used was a valid and reliable test of the students’ CT skills. The results showed that the GBL model with AR-based games significantly improved students’ CT skills. The students who received this approach showed more significant improvements in CT compared to traditional teaching methods. These results highlight the potential benefits of integrating AR technology into education to enhance students’ CT skills. This encourages educators and curriculum developers to view AR as an effective alternative for supporting students’ CT. The outcomes of this research indicate a significant advantage in using AR as a tool to promote CT among students. It could create more engaging and interactive learning environments.
学生批判性思维能力的重要性在于他们能够积极地评估、评价信息，并对信息做出深思熟虑的反应。本研究调查了在数学学习游戏中使用增强现实技术（AR）的游戏式学习（GBL）模式对初中八年级学生批判性思维能力的影响。本研究特别关注数学中的多面体。本研究采用准实验设计来实现研究目标。本研究涉及 77 名学生，分为由 40 名学生组成的实验组和由 37 名学生组成的对照组。使用的研究工具是对学生 CT 技能进行的有效而可靠的测试。结果表明，基于 AR 游戏的 GBL 模式明显提高了学生的 CT 技能。与传统教学方法相比，接受这种方法的学生在 CT 方面有更明显的提高。这些结果凸显了将 AR 技术融入教学以提高学生 CT 技能的潜在益处。这鼓励教育工作者和课程开发人员将 AR 视为支持学生 CT 的有效替代方法。本研究的结果表明，将 AR 用作促进学生 CT 的工具具有显著优势。它可以创造更具吸引力和互动性的学习环境。
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Pub Date : 2024-04-09DOI: 10.3991/ijim.v18i07.46267
Mohamad Aidiid Hafifi Saedan, Murizah Kassim, Azalina Farina Abd Aziz
This study presents the development of a mobile identification system that detects biological butterfly characteristics through deep learning by capturing images. The challenge identified is that butterfly identification and recognition are difficult tasks because there are too many species, and it is hard to classify the types of butterfly species. Butterflies are also difficult to differentiate from each other, and limited studies are done using computer database referrals for butterflies’ characterization. This study aims to develop an automated computer program to easily identify the species of butterflies. Deep learning in image processing is programmed, which can control the qualification, segmentation, and classification of images and automatically detect butterfly characterization. The design system consists of three stages: capture, feature extraction, and butterfly recognition. Then, multiple recognition clues such as shape, color, texture, and size are extracted and analyzed to analyze and recognize the butterfly. This approach is faster and less complex than the previous approach. The result successfully presents a convolutional neural network (CNN) to classify images after training and characterization. The graphics processing unit (GPU) that trains the image dataset presents 86% image accuracy in the allocated time. This research is significant in such a way that new butterfly species will be automatically collected and stored on the online server. The information could be treasured as a valuable butterfly database.
{"title":"Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image","authors":"Mohamad Aidiid Hafifi Saedan, Murizah Kassim, Azalina Farina Abd Aziz","doi":"10.3991/ijim.v18i07.46267","DOIUrl":"https://doi.org/10.3991/ijim.v18i07.46267","url":null,"abstract":"This study presents the development of a mobile identification system that detects biological butterfly characteristics through deep learning by capturing images. The challenge identified is that butterfly identification and recognition are difficult tasks because there are too many species, and it is hard to classify the types of butterfly species. Butterflies are also difficult to differentiate from each other, and limited studies are done using computer database referrals for butterflies’ characterization. This study aims to develop an automated computer program to easily identify the species of butterflies. Deep learning in image processing is programmed, which can control the qualification, segmentation, and classification of images and automatically detect butterfly characterization. The design system consists of three stages: capture, feature extraction, and butterfly recognition. Then, multiple recognition clues such as shape, color, texture, and size are extracted and analyzed to analyze and recognize the butterfly. This approach is faster and less complex than the previous approach. The result successfully presents a convolutional neural network (CNN) to classify images after training and characterization. The graphics processing unit (GPU) that trains the image dataset presents 86% image accuracy in the allocated time. This research is significant in such a way that new butterfly species will be automatically collected and stored on the online server. The information could be treasured as a valuable butterfly database.","PeriodicalId":507995,"journal":{"name":"International Journal of Interactive Mobile Technologies (iJIM)","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140722979","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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