Pub Date : 2018-11-01DOI: 10.1109/MITE.2018.8747129
M. Shukla
Social Networking Sites (SNS) is deeply penetrating into today’s culture and lifestyle of humans. It has not only left its marks in social life but also in education as well in the form of blended learning. Researchers are now striving to utilize SNS as pedagogy re-engineering tool for academics. In this study, an attempt is made to understand the perception of students and teacher community on the inclusion of SNS as a pedagogy tool into Higher Education Institutes (HEIs). For this purpose, a questionnaire survey was conducted into two HEIs of Chhattisgarh, one private and one government institute. The survey included students as well as teachers with a total of 255 stakeholders of the two HEIs. The findings of this research provide an insight to the quest of including SNS into HEIs.
{"title":"Inclusion of Social Networking Sites into Higher Education: An empirical study from Chhattisgarh","authors":"M. Shukla","doi":"10.1109/MITE.2018.8747129","DOIUrl":"https://doi.org/10.1109/MITE.2018.8747129","url":null,"abstract":"Social Networking Sites (SNS) is deeply penetrating into today’s culture and lifestyle of humans. It has not only left its marks in social life but also in education as well in the form of blended learning. Researchers are now striving to utilize SNS as pedagogy re-engineering tool for academics. In this study, an attempt is made to understand the perception of students and teacher community on the inclusion of SNS as a pedagogy tool into Higher Education Institutes (HEIs). For this purpose, a questionnaire survey was conducted into two HEIs of Chhattisgarh, one private and one government institute. The survey included students as well as teachers with a total of 255 stakeholders of the two HEIs. The findings of this research provide an insight to the quest of including SNS into HEIs.","PeriodicalId":426754,"journal":{"name":"2018 IEEE 6th International Conference on MOOCs, Innovation and Technology in Education (MITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127489987","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 : 2018-11-01DOI: 10.1109/MITE.2018.8747063
Bhrigu Garg, Anika
In today’s e-learning based educational scenarios, lot of efforts in terms of time and manpower are required by the MOOC instructors for the generation of transcripts. This research study is focused on the efficient and correct punctuation prediction in the process of automated generation of these transcripts. Various deep learning based and other commonly used punctuation prediction techniques and models existing in the literature have been identified and analyzed for the educational domain videos. The hybrid model of Convolution Neural Networks and Bidirectional Long Short Term Memory ensembled with the acoustic model outperformed other models. It yielded an accuracy of 93.56 percent, recall of 56.15 percent and precision of 63.69 percent. This study also proposed a generalized architecture for efficient punctuation prediction.
{"title":"Analysis of Punctuation Prediction Models for Automated Transcript Generation in MOOC Videos","authors":"Bhrigu Garg, Anika","doi":"10.1109/MITE.2018.8747063","DOIUrl":"https://doi.org/10.1109/MITE.2018.8747063","url":null,"abstract":"In today’s e-learning based educational scenarios, lot of efforts in terms of time and manpower are required by the MOOC instructors for the generation of transcripts. This research study is focused on the efficient and correct punctuation prediction in the process of automated generation of these transcripts. Various deep learning based and other commonly used punctuation prediction techniques and models existing in the literature have been identified and analyzed for the educational domain videos. The hybrid model of Convolution Neural Networks and Bidirectional Long Short Term Memory ensembled with the acoustic model outperformed other models. It yielded an accuracy of 93.56 percent, recall of 56.15 percent and precision of 63.69 percent. This study also proposed a generalized architecture for efficient punctuation prediction.","PeriodicalId":426754,"journal":{"name":"2018 IEEE 6th International Conference on MOOCs, Innovation and Technology in Education (MITE)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130116378","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 : 2018-07-24DOI: 10.1109/MITE.2018.8747128
Ridima Verma, Anika
In higher education scenarios, elective courses sought to provide a deeper insight of the trending advancements in the field of specialization for undergraduate students. So, choice of elective subjects during the pre-final or final year of the undergraduates play a crucial role as they help in shaping their career or area of specialization for future research. However, there exist numerous gaps and concerns that arise due to mismatch of the elective courses pre-requisites and the student’s possessed skills-set which result in degraded quality as well as student academic performance. This research study focuses on filling in these gaps by predicting the marks in different elective subjects for the current cohort of students, beforehand, as well as side by side preserving their explicit subject preferences. With the help of the proposed methodology an accuracy of 88% was achieved for providing efficient bilateral elective course recommendations.
{"title":"Applying Predictive Analytics in Elective Course Recommender System while preserving Student Course Preferences","authors":"Ridima Verma, Anika","doi":"10.1109/MITE.2018.8747128","DOIUrl":"https://doi.org/10.1109/MITE.2018.8747128","url":null,"abstract":"In higher education scenarios, elective courses sought to provide a deeper insight of the trending advancements in the field of specialization for undergraduate students. So, choice of elective subjects during the pre-final or final year of the undergraduates play a crucial role as they help in shaping their career or area of specialization for future research. However, there exist numerous gaps and concerns that arise due to mismatch of the elective courses pre-requisites and the student’s possessed skills-set which result in degraded quality as well as student academic performance. This research study focuses on filling in these gaps by predicting the marks in different elective subjects for the current cohort of students, beforehand, as well as side by side preserving their explicit subject preferences. With the help of the proposed methodology an accuracy of 88% was achieved for providing efficient bilateral elective course recommendations.","PeriodicalId":426754,"journal":{"name":"2018 IEEE 6th International Conference on MOOCs, Innovation and Technology in Education (MITE)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122843761","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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