Pub Date : 2022-11-07DOI: 10.1109/ITHET56107.2022.10031642
Thomas Fuhrmann
Due to the various demands for lecturers, there is only a limited time to prepare lectures and lab courses. Therefore, it is necessary to invest the time target-oriented for optimal student learning success. A theoretic model is developed to structure course preparation work regarding scientific content, didactic preparation, and course presentation. Model parameters have to be chosen for each course depending on topic complexity, the lecturer’s prior knowledge, and the already available preparation from the prior semesters. With these parameters, a course preparation model for a complete semester is developed. Analytic models for different optimization strategies are introduced according to the overall goal of the lecturer. Numerical optimization is done to find the appropriate course preparation times to reach an optimal course preparation for high student learning success. It is seen that due to the different optimization strategies, the preparation time results vary and no single truth is given. But this optimization system gives hints on how to invest preparation time target-oriented for high student learning success.
{"title":"Course Preparation Time Optimization System for Improved Didactic Outcome","authors":"Thomas Fuhrmann","doi":"10.1109/ITHET56107.2022.10031642","DOIUrl":"https://doi.org/10.1109/ITHET56107.2022.10031642","url":null,"abstract":"Due to the various demands for lecturers, there is only a limited time to prepare lectures and lab courses. Therefore, it is necessary to invest the time target-oriented for optimal student learning success. A theoretic model is developed to structure course preparation work regarding scientific content, didactic preparation, and course presentation. Model parameters have to be chosen for each course depending on topic complexity, the lecturer’s prior knowledge, and the already available preparation from the prior semesters. With these parameters, a course preparation model for a complete semester is developed. Analytic models for different optimization strategies are introduced according to the overall goal of the lecturer. Numerical optimization is done to find the appropriate course preparation times to reach an optimal course preparation for high student learning success. It is seen that due to the different optimization strategies, the preparation time results vary and no single truth is given. But this optimization system gives hints on how to invest preparation time target-oriented for high student learning success.","PeriodicalId":125795,"journal":{"name":"2022 20th International Conference on Information Technology Based Higher Education and Training (ITHET)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125526062","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 : 2022-11-07DOI: 10.1109/ITHET56107.2022.10031982
P. Amoako, I. Osunmakinde
Bandwidth resource in open distance electronic learning platform is scarce when services performed by many users contend for bandwidth, causing congestion in the network. The challenge has increased tremendously since almost all academic institutions perform activities online during the pandemic. This paper investigates the inherent workload constraints among open-distance electronic learning (ODeL) services competing for scarce resources and intends to forecast future bandwidth demands to prevent online class disruptions. A predictive framework of bandwidth management, which integrates a sustainable hidden Markov model (HMM) and a normalization policy, coupled with SolarWinds technology for prior network data feeder, is developed. A sustainable HMM $alpha$ emerges from three HMM candidates based on test priorities on bandwidth demands. Compared to four popular methods, detailed experiments on the proposed model revealed performance analysis of error metrics below 0.5 at peak and off-peak periods. The emerged HMM $alpha$ reliably predicted bandwidth capacities required to sustain the competing ODeL services with an accuracy of 94%.
{"title":"Intelligent Bandwidth Planner Enhancing Learning Technologies in Constrained Distance Learning Environments – a Pandemic Response","authors":"P. Amoako, I. Osunmakinde","doi":"10.1109/ITHET56107.2022.10031982","DOIUrl":"https://doi.org/10.1109/ITHET56107.2022.10031982","url":null,"abstract":"Bandwidth resource in open distance electronic learning platform is scarce when services performed by many users contend for bandwidth, causing congestion in the network. The challenge has increased tremendously since almost all academic institutions perform activities online during the pandemic. This paper investigates the inherent workload constraints among open-distance electronic learning (ODeL) services competing for scarce resources and intends to forecast future bandwidth demands to prevent online class disruptions. A predictive framework of bandwidth management, which integrates a sustainable hidden Markov model (HMM) and a normalization policy, coupled with SolarWinds technology for prior network data feeder, is developed. A sustainable HMM $alpha$ emerges from three HMM candidates based on test priorities on bandwidth demands. Compared to four popular methods, detailed experiments on the proposed model revealed performance analysis of error metrics below 0.5 at peak and off-peak periods. The emerged HMM $alpha$ reliably predicted bandwidth capacities required to sustain the competing ODeL services with an accuracy of 94%.","PeriodicalId":125795,"journal":{"name":"2022 20th International Conference on Information Technology Based Higher Education and Training (ITHET)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115237200","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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