Pub Date : 2022-07-01DOI: 10.1109/ICALT55010.2022.00106
Jennifer Rogers
Recent reports indicate increased organizational appetite and spend in the energy industry in both the areas of operational risk management training and enablement and in extended reality hardware and software, as part of larger automation and digital transformation initiatives. Furthermore, recent advances in immersive technology, along with more dispersed, asynchronous working conditions due to COVID, have resulted in scalable, immersive simulations that more and more closely resemble real world environments. While recent standards have defined JSON syntax appropriate for tracking and measuring human behavior data in generic learning environments (IEEE P9274.1) and in a manner that more closely approximates human behavior in the workplace, as typically tracked in operational risk management systems, no risk-based ontology has yet been defined that more closely crosswalks and correlates data from simulated environment systems to those in operational environments. Thus, the true efficacy of extended reality-based risk mitigation training cannot be fully measured. In this effort, a risk-based ontology and matrix was constructed in accordance with the xAPI standard syntax and allowable extensions and was utilized to transform a subset of historical data from simulated operational risk-based scenarios from the energy industry. Transformed data from this initial subset closely approximated operational risk reporting data and provided insights into human behavior data in simulated environments that can be easily compared and correlated to existing operational excellence and risk mitigation KPIs. Implications for mapping of additional advanced data from simulated environments in larger, more complex datasets, such as eye tracking and biometrics, were also considered and explored.
{"title":"Standardized Risk Mitigation Measurement in Extended Reality Environments Utilizing the IEEE Experience API (xAPI) Standard","authors":"Jennifer Rogers","doi":"10.1109/ICALT55010.2022.00106","DOIUrl":"https://doi.org/10.1109/ICALT55010.2022.00106","url":null,"abstract":"Recent reports indicate increased organizational appetite and spend in the energy industry in both the areas of operational risk management training and enablement and in extended reality hardware and software, as part of larger automation and digital transformation initiatives. Furthermore, recent advances in immersive technology, along with more dispersed, asynchronous working conditions due to COVID, have resulted in scalable, immersive simulations that more and more closely resemble real world environments. While recent standards have defined JSON syntax appropriate for tracking and measuring human behavior data in generic learning environments (IEEE P9274.1) and in a manner that more closely approximates human behavior in the workplace, as typically tracked in operational risk management systems, no risk-based ontology has yet been defined that more closely crosswalks and correlates data from simulated environment systems to those in operational environments. Thus, the true efficacy of extended reality-based risk mitigation training cannot be fully measured. In this effort, a risk-based ontology and matrix was constructed in accordance with the xAPI standard syntax and allowable extensions and was utilized to transform a subset of historical data from simulated operational risk-based scenarios from the energy industry. Transformed data from this initial subset closely approximated operational risk reporting data and provided insights into human behavior data in simulated environments that can be easily compared and correlated to existing operational excellence and risk mitigation KPIs. Implications for mapping of additional advanced data from simulated environments in larger, more complex datasets, such as eye tracking and biometrics, were also considered and explored.","PeriodicalId":221464,"journal":{"name":"2022 International Conference on Advanced Learning Technologies (ICALT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131896551","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 : 2022-07-01DOI: 10.1109/ICALT55010.2022.00071
Henrik Køhler Simonsen
AI-generated text production is becoming increasingly important in many industries, and it has already brought about dramatic changes in the ways we write texts and generate content. The article draws on empirical data from a descriptive-analytical study involving 70 test subjects. The population comprised 115 test persons, who received an e-mail with instructions. A sample of 70 test subjects participated in the study. First, the test subjects were asked to test a specific AI text generator (ATG) and conduct three prompting operations with the same linguistic content. Second, having tested the ATG, the test subjects were asked to participate in a questionnaire with ten questions focusing on how they experienced the performance of the ATG and how they worked with the ATG. The majority of the test subjects found that the tested ATG was easy to use when producing texts. When asked about the perceived quality of the AI-generated content, the respondents were not impressed with the quality and indicated that they needed to perform several editing operations. The data also indicate that ATGs need help before, during and after. This paper presents a three-phase editing framework, which can be used when using and teaching ATGs.
{"title":"AI Text Generators and Text Producers","authors":"Henrik Køhler Simonsen","doi":"10.1109/ICALT55010.2022.00071","DOIUrl":"https://doi.org/10.1109/ICALT55010.2022.00071","url":null,"abstract":"AI-generated text production is becoming increasingly important in many industries, and it has already brought about dramatic changes in the ways we write texts and generate content. The article draws on empirical data from a descriptive-analytical study involving 70 test subjects. The population comprised 115 test persons, who received an e-mail with instructions. A sample of 70 test subjects participated in the study. First, the test subjects were asked to test a specific AI text generator (ATG) and conduct three prompting operations with the same linguistic content. Second, having tested the ATG, the test subjects were asked to participate in a questionnaire with ten questions focusing on how they experienced the performance of the ATG and how they worked with the ATG. The majority of the test subjects found that the tested ATG was easy to use when producing texts. When asked about the perceived quality of the AI-generated content, the respondents were not impressed with the quality and indicated that they needed to perform several editing operations. The data also indicate that ATGs need help before, during and after. This paper presents a three-phase editing framework, which can be used when using and teaching ATGs.","PeriodicalId":221464,"journal":{"name":"2022 International Conference on Advanced Learning Technologies (ICALT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133822581","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 : 2022-07-01DOI: 10.1109/ICALT55010.2022.00029
Shin-Yan Chong, Fang-Fang Chua, T. Lim
In the big-data era, massive Open Educational Resources (OERs) can be obtained from the Internet regardless of location or time constraints. Researchers have discussed microlearning as a service to improve learning effectiveness. However, the emergence of OERs leads to the challenge of searching for appropriate and relevant microlearning resources. In this paper, an automated video classification approach named “MVR-CLS” is proposed to organize and classify microlearning resources, so that the learners can browse for learning resources in a manageable way. Speech-To-Text data mining technique is applied to transcribe a learning video and to further analyze the video content. A 3-tier learning category structure is proposed to organize a collection of microlearning videos into appropriate learning categories. “MVR-CLS” has shown the capability to classify the microlearning videos into a finer-grained learning category as compared to the existing work. To evaluate the accuracy of the proposed approach, the classification result is validated against to the metadata of the OERs. The classification result can promote better fit of learners’ interests for content recommendations and thus enhancing the recommendation accuracy in future work.
{"title":"MVR-CLS: An Automated Approach for Effective Classification of Microlearning Video Resources","authors":"Shin-Yan Chong, Fang-Fang Chua, T. Lim","doi":"10.1109/ICALT55010.2022.00029","DOIUrl":"https://doi.org/10.1109/ICALT55010.2022.00029","url":null,"abstract":"In the big-data era, massive Open Educational Resources (OERs) can be obtained from the Internet regardless of location or time constraints. Researchers have discussed microlearning as a service to improve learning effectiveness. However, the emergence of OERs leads to the challenge of searching for appropriate and relevant microlearning resources. In this paper, an automated video classification approach named “MVR-CLS” is proposed to organize and classify microlearning resources, so that the learners can browse for learning resources in a manageable way. Speech-To-Text data mining technique is applied to transcribe a learning video and to further analyze the video content. A 3-tier learning category structure is proposed to organize a collection of microlearning videos into appropriate learning categories. “MVR-CLS” has shown the capability to classify the microlearning videos into a finer-grained learning category as compared to the existing work. To evaluate the accuracy of the proposed approach, the classification result is validated against to the metadata of the OERs. The classification result can promote better fit of learners’ interests for content recommendations and thus enhancing the recommendation accuracy in future work.","PeriodicalId":221464,"journal":{"name":"2022 International Conference on Advanced Learning Technologies (ICALT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123206121","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 : 2022-07-01DOI: 10.1109/ICALT55010.2022.00094
Meng-Xuan Xie, Yu-Wei Wu, Gwo-Dong Chen, Jen-Hang Wang, Su-Hang Yang
In the current digital situational learning, such as digital game-based learning or Second Life, learners use virtual characters to enter the situational learning, instead of using their own bodies. In addition, many subject matters requiring situational learning need a combination of subject knowledge, body language, and emotional expression, and require students to conduct group learning. However, most of the current situational systems can only provide evaluation and feedback on text or language, and cannot provide feedback on body movements and emotional expressions. To address these issues, this study establishes a situational learning system that enables students to enter the learning situation with their whole body and in small groups. Furthermore, the cognitive services of artificial intelligence are also adopted to conduct immediate evaluation and feedback on students’ learning performance, such as language, body movement, and emotional display, and students’ learning outcomes can be displayed in front of their peers. The experimental results show that the proposed situational learning system can improve students’ learning effect for subject matters that need to combine body movements and emotional expression.
{"title":"An immersive situational group learning system with body movement and emotion recognition combined with subject knowledge","authors":"Meng-Xuan Xie, Yu-Wei Wu, Gwo-Dong Chen, Jen-Hang Wang, Su-Hang Yang","doi":"10.1109/ICALT55010.2022.00094","DOIUrl":"https://doi.org/10.1109/ICALT55010.2022.00094","url":null,"abstract":"In the current digital situational learning, such as digital game-based learning or Second Life, learners use virtual characters to enter the situational learning, instead of using their own bodies. In addition, many subject matters requiring situational learning need a combination of subject knowledge, body language, and emotional expression, and require students to conduct group learning. However, most of the current situational systems can only provide evaluation and feedback on text or language, and cannot provide feedback on body movements and emotional expressions. To address these issues, this study establishes a situational learning system that enables students to enter the learning situation with their whole body and in small groups. Furthermore, the cognitive services of artificial intelligence are also adopted to conduct immediate evaluation and feedback on students’ learning performance, such as language, body movement, and emotional display, and students’ learning outcomes can be displayed in front of their peers. The experimental results show that the proposed situational learning system can improve students’ learning effect for subject matters that need to combine body movements and emotional expression.","PeriodicalId":221464,"journal":{"name":"2022 International Conference on Advanced Learning Technologies (ICALT)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122072295","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 : 2022-07-01DOI: 10.1109/ICALT55010.2022.00118
Marcelo Guerra Hahn, S. Baldiris, Luis de la Fuente Valentín
The increase in usage of online learning systems, has caused a renewed interest in using computers to provide more support to the student’s learning experiences, allow instructors to focus on activities that require human intervention, and enable courses with a large number of students to help them achieve the learning objectives. One experience that has been particularly complex to emulate in online environments is assignment grading. The grading cycle tends to be a weak point in the experience. Among its drawbacks, two main issues are potential turnaround time and that assignments tend to provide only one opportunity to show understanding of the course content. In the context of programming assignments, this experience is particularly problematic as the programming cycle tends to be an iterative process. This paper discusses the initial implementation of an automatic scoring and feedback system for programming assignments. The system includes feedback on syntax, semantics, and code structure. We explain the architecture of the system and the results of an experiment run with 20 students that shows the effects of the system. In the experiment, we observed that the feedback system improves student performance in the assignment as measured by the grade assigned to them.
{"title":"LUD: An Automatic Scoring and Feedback System for Programming Assignments","authors":"Marcelo Guerra Hahn, S. Baldiris, Luis de la Fuente Valentín","doi":"10.1109/ICALT55010.2022.00118","DOIUrl":"https://doi.org/10.1109/ICALT55010.2022.00118","url":null,"abstract":"The increase in usage of online learning systems, has caused a renewed interest in using computers to provide more support to the student’s learning experiences, allow instructors to focus on activities that require human intervention, and enable courses with a large number of students to help them achieve the learning objectives. One experience that has been particularly complex to emulate in online environments is assignment grading. The grading cycle tends to be a weak point in the experience. Among its drawbacks, two main issues are potential turnaround time and that assignments tend to provide only one opportunity to show understanding of the course content. In the context of programming assignments, this experience is particularly problematic as the programming cycle tends to be an iterative process. This paper discusses the initial implementation of an automatic scoring and feedback system for programming assignments. The system includes feedback on syntax, semantics, and code structure. We explain the architecture of the system and the results of an experiment run with 20 students that shows the effects of the system. In the experiment, we observed that the feedback system improves student performance in the assignment as measured by the grade assigned to them.","PeriodicalId":221464,"journal":{"name":"2022 International Conference on Advanced Learning Technologies (ICALT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128548595","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 : 2022-07-01DOI: 10.1109/ICALT55010.2022.00098
Sylvio Rüdian, Niels Pinkwart
In this paper, we use a Generative Adversarial Network (GAN) as a sequence generator for language learning online courses. Therefore, we cluster a very small dataset of manually created training samples to derive rules. Then, we train a GAN that can mimic rule-based sequences, where we use our derived rules to evaluate generated samples. We enhance our approach by a parameter that course creators can select deviations they want to have in new sequences without manual adjustments. The resulting sequences follow the core structure of the small sample set. Based on deviations of the generated new learning paths, new combinations of methods can be used that course creators did not previously have in mind. This opens up a new way to generate course sequences without the need to model many alternative learning paths for adaptions.
{"title":"Generating Sequences for Online Courses using a GAN based on a small Sample Set","authors":"Sylvio Rüdian, Niels Pinkwart","doi":"10.1109/ICALT55010.2022.00098","DOIUrl":"https://doi.org/10.1109/ICALT55010.2022.00098","url":null,"abstract":"In this paper, we use a Generative Adversarial Network (GAN) as a sequence generator for language learning online courses. Therefore, we cluster a very small dataset of manually created training samples to derive rules. Then, we train a GAN that can mimic rule-based sequences, where we use our derived rules to evaluate generated samples. We enhance our approach by a parameter that course creators can select deviations they want to have in new sequences without manual adjustments. The resulting sequences follow the core structure of the small sample set. Based on deviations of the generated new learning paths, new combinations of methods can be used that course creators did not previously have in mind. This opens up a new way to generate course sequences without the need to model many alternative learning paths for adaptions.","PeriodicalId":221464,"journal":{"name":"2022 International Conference on Advanced Learning Technologies (ICALT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128715702","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 : 2022-07-01DOI: 10.1109/ICALT55010.2022.00017
Sylvio Rüdian, Niels Pinkwart
Online courses have very high dropout rates worldwide. Learners are demotivated based on bad learning experiences. While some factors of online courses could be optimized for all learners, e.g. the quality, it is essential to note that a one-size-fits-all environment is not existing. Some learners are comfortable with certain methods while others may not. In the paper, we identified five learner preferences that can be used to adapt teaching methods in online courses. We provide a 10-item questionnaire, validate it based on exploratory factor analysis, and examine whether learners differ in preferences.
{"title":"Do learners really have different preferences?","authors":"Sylvio Rüdian, Niels Pinkwart","doi":"10.1109/ICALT55010.2022.00017","DOIUrl":"https://doi.org/10.1109/ICALT55010.2022.00017","url":null,"abstract":"Online courses have very high dropout rates worldwide. Learners are demotivated based on bad learning experiences. While some factors of online courses could be optimized for all learners, e.g. the quality, it is essential to note that a one-size-fits-all environment is not existing. Some learners are comfortable with certain methods while others may not. In the paper, we identified five learner preferences that can be used to adapt teaching methods in online courses. We provide a 10-item questionnaire, validate it based on exploratory factor analysis, and examine whether learners differ in preferences.","PeriodicalId":221464,"journal":{"name":"2022 International Conference on Advanced Learning Technologies (ICALT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128661530","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 : 2022-07-01DOI: 10.1109/ICALT55010.2022.00103
Chunping Li, Soonja Yeom, Julian R. Dermoudy, K. Salas
The rapid development of VR technology in training and learning is based on the assumption that it is beneficial for skill training within an immersive environment. However, extra cognitive load may be induced due to the additional sensory information and hence learning ability might be affected. In this study, we examined and compared the impact of cognitive load and task performance in real-world and VR environments through an empirical quadrant model. Forty-six participants completed the tasks with and without the secondary task in realworld and VR environments. The detection response task (DRT), as the secondary task, was adopted to estimate cognitive load based on response time and omission rate. No statistically significant differences were found in cognitive load and task performance in the comparison of VR and non-VR environment settings. There was an encouraging trend observed that VR environments have some advantages over the real-world, such as a higher level of immersion, which suggests that VR can benefit trainees with improved concentration levels and task performance. As evidenced by the variation in performance between females and males in our study, it appears that females tend to perform less well in VR environments, with a slightly higher cognitive load.
{"title":"Cognitive Load Measurement in the Impact of VR Intervention in Learning","authors":"Chunping Li, Soonja Yeom, Julian R. Dermoudy, K. Salas","doi":"10.1109/ICALT55010.2022.00103","DOIUrl":"https://doi.org/10.1109/ICALT55010.2022.00103","url":null,"abstract":"The rapid development of VR technology in training and learning is based on the assumption that it is beneficial for skill training within an immersive environment. However, extra cognitive load may be induced due to the additional sensory information and hence learning ability might be affected. In this study, we examined and compared the impact of cognitive load and task performance in real-world and VR environments through an empirical quadrant model. Forty-six participants completed the tasks with and without the secondary task in realworld and VR environments. The detection response task (DRT), as the secondary task, was adopted to estimate cognitive load based on response time and omission rate. No statistically significant differences were found in cognitive load and task performance in the comparison of VR and non-VR environment settings. There was an encouraging trend observed that VR environments have some advantages over the real-world, such as a higher level of immersion, which suggests that VR can benefit trainees with improved concentration levels and task performance. As evidenced by the variation in performance between females and males in our study, it appears that females tend to perform less well in VR environments, with a slightly higher cognitive load.","PeriodicalId":221464,"journal":{"name":"2022 International Conference on Advanced Learning Technologies (ICALT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121462269","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 : 2022-07-01DOI: 10.1109/ICALT55010.2022.00075
Carla Chu Pérez
To overcome most of the challenges that a synchronous online class generated by the pandemic situation, the use of a Learning Management System (LMS) was indispensable. Blackboard Learn Ultra is the e-learning platform used by the Peruvian University of Applied Sciences and its Translation and Interpretation program, specifically used to develop Chinese TI2, the course selected for this study.In this study, we explore an asynchronous activity linked to another digital tool, such as: Padlet for collaborative assignments focused on Chinese production, comprehension, and translation. This study aims to enhance meaningful activities for learners of Chinese language, providing them tools, tasks and interactions that could be contextualized and adapted into a specific purpose. In addition, we present qualitative data from the structure, communication, monitoring to evaluating the results of these activities through the mentioned LMS.
{"title":"Exploring an E-Language Learning Activity for a Specific Purpose: A Case Study of a Translation and Interpretation Department in Peru","authors":"Carla Chu Pérez","doi":"10.1109/ICALT55010.2022.00075","DOIUrl":"https://doi.org/10.1109/ICALT55010.2022.00075","url":null,"abstract":"To overcome most of the challenges that a synchronous online class generated by the pandemic situation, the use of a Learning Management System (LMS) was indispensable. Blackboard Learn Ultra is the e-learning platform used by the Peruvian University of Applied Sciences and its Translation and Interpretation program, specifically used to develop Chinese TI2, the course selected for this study.In this study, we explore an asynchronous activity linked to another digital tool, such as: Padlet for collaborative assignments focused on Chinese production, comprehension, and translation. This study aims to enhance meaningful activities for learners of Chinese language, providing them tools, tasks and interactions that could be contextualized and adapted into a specific purpose. In addition, we present qualitative data from the structure, communication, monitoring to evaluating the results of these activities through the mentioned LMS.","PeriodicalId":221464,"journal":{"name":"2022 International Conference on Advanced Learning Technologies (ICALT)","volume":"556 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116274037","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 : 2022-07-01DOI: 10.1109/ICALT55010.2022.00063
Siddharth Srivastava, T. Prabhakar
The teaching trend is shifting towards online learning due to the COVID-19 pandemic. Due to this, micro-lectures (MIL) are gaining popularity. Generally, assignments follow MIL to make learners comfortable with the concept explained in the MIL. So, the traditional micro-lecturing process (MILP) aims to enhance students’ concept understanding skills. However, according to Bloom’s taxonomy, understanding, application, and error handling are three fundamental skills required for students’ overall academic development. Hence, traditional MILs fail to nurture the other two skills. So, is it possible to integrate application and error handling skills in the traditional micro-lecturing process? This research provides a handle to address this research question by proposing the micro-tutorial strategy. This strategy modifies the traditional MILP by doping it with micro-tutorials (MTUT). This modified MILP is called the micro-tutorial-based MILP. This process can simultaneously enhance students’ understanding, application, and error handling skills. To prove the validity of the proposed strategy, we floated a ‘C’ programming course in which we lectured students using the proposed strategy and observed fantastic results. We believe that this research opens new dimensions for designing pedagogically effective MILs.
{"title":"Micro-Tutorial: A Strategy for Integrating Error Handling and Concept Application Skills in Traditional Micro-Lecturing Process","authors":"Siddharth Srivastava, T. Prabhakar","doi":"10.1109/ICALT55010.2022.00063","DOIUrl":"https://doi.org/10.1109/ICALT55010.2022.00063","url":null,"abstract":"The teaching trend is shifting towards online learning due to the COVID-19 pandemic. Due to this, micro-lectures (MIL) are gaining popularity. Generally, assignments follow MIL to make learners comfortable with the concept explained in the MIL. So, the traditional micro-lecturing process (MILP) aims to enhance students’ concept understanding skills. However, according to Bloom’s taxonomy, understanding, application, and error handling are three fundamental skills required for students’ overall academic development. Hence, traditional MILs fail to nurture the other two skills. So, is it possible to integrate application and error handling skills in the traditional micro-lecturing process? This research provides a handle to address this research question by proposing the micro-tutorial strategy. This strategy modifies the traditional MILP by doping it with micro-tutorials (MTUT). This modified MILP is called the micro-tutorial-based MILP. This process can simultaneously enhance students’ understanding, application, and error handling skills. To prove the validity of the proposed strategy, we floated a ‘C’ programming course in which we lectured students using the proposed strategy and observed fantastic results. We believe that this research opens new dimensions for designing pedagogically effective MILs.","PeriodicalId":221464,"journal":{"name":"2022 International Conference on Advanced Learning Technologies (ICALT)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115279233","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：