{"title":"Effectiveness of Mobile Learning at TCE, India: A Learner Perspective","authors":"P. Karthikeyan, V. UmaK., S. Pudumalar","doi":"10.1109/T4E.2015.8","DOIUrl":"https://doi.org/10.1109/T4E.2015.8","url":null,"abstract":"This research study provides a quantitative survey of different learners on effectiveness of mobile learning at Thiagarajar College of Engineering (TCE), Madurai, India. The objective of this survey is to incorporate mobile learning in complex subjects for obtaining high level knowledge among the various types of student learners at TCE. Initially, the survey is made to identify the different types of learners in a class. Then several apps, videos are chosen based on some complex subjects in IT curriculum of TCE for learning few topics through mobile devices. Another survey has made among the identified learners about mobile learning. On analysis, almost more than 75 percent of learners of all types agreed and strongly agreed for the effectiveness of mobile learning on their curriculum subjects at TCE.","PeriodicalId":215344,"journal":{"name":"2015 IEEE Seventh International Conference on Technology for Education (T4E)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131733867","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}
{"title":"Technology Enabled Learning (TEL) to Improve Pronunciation in the Students of Professional Courses -- A Study","authors":"Madhavi Kesari, I. A. K. Reddy","doi":"10.1109/T4E.2015.17","DOIUrl":"https://doi.org/10.1109/T4E.2015.17","url":null,"abstract":"This study seeks to assess the efficacy of technology enabled learning (TEL) to teach pronunciation to undergraduate students pursuing first year Engineering programme using 'Pronunciation Power' software in the English Language Communication Skills Lab (ELCS). The students were exposed to supra segmental features of the language through authentic tasks such as inspirational speeches, interviews, debates etc. Interestingly the findings revealed that TEL helped the students improve their accent, intonation and style of speaking better than the teacher given inputs. The performance of the students has been examined using the speech analysis software in addition to the assessment of oral presentations made by the students in the ELCS Lab.","PeriodicalId":215344,"journal":{"name":"2015 IEEE Seventh International Conference on Technology for Education (T4E)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116844435","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}
K. Govindarajan, Vivekanandan Kumar, David Boulanger, Kinshuk
{"title":"Learning Analytics Solution for Reducing Learners' Course Failure Rate","authors":"K. Govindarajan, Vivekanandan Kumar, David Boulanger, Kinshuk","doi":"10.1109/T4E.2015.14","DOIUrl":"https://doi.org/10.1109/T4E.2015.14","url":null,"abstract":"In recent years, learning analytics solutions have highly appealed to the higher education community who mainly focuses on improving the learning process, self-regulated learning skills, and learners' success rate. Learning analytics has to deal with continuous data, however, conventional data mining algorithms are not readily applicable to handle the continuous incoming of learners' data. In order to cope with these scenarios, the proposed learning analytics aimed to manage the continuous data, perform the clustering process using the optimization approach, detect the 'at-risk' learners' who are in a course failure situation, and generate signals to learners and teachers. Based on the predicted outcome, the proposed system identifies and adapts the learning activities and learning contents to help learners find their way out of their learning difficulties and course failure situation. The experiments were conducted to analyze the performance of the proposed work using the simulated learners' data. The experimental results provide empirical evidence that the proposed work reduces the course failure rate and improves learners' success rate.","PeriodicalId":215344,"journal":{"name":"2015 IEEE Seventh International Conference on Technology for Education (T4E)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127641446","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}
Ankit Sharma, Udita Prajapati, Vivekanandan S. Kumar, Kinshuk
{"title":"Analytics Using Activity Trackers in the Field of Education","authors":"Ankit Sharma, Udita Prajapati, Vivekanandan S. Kumar, Kinshuk","doi":"10.1109/T4E.2015.7","DOIUrl":"https://doi.org/10.1109/T4E.2015.7","url":null,"abstract":"Today activity trackers are used as a major tool for tracking, monitoring, and quantifying health aspects in the form of physiological data. Such wearable trackers can be efficiently utilised in other fields apart from healthcare. This paper shows how an activity tracker can be used to create a real time application for monitoring the activity of a student by parents as well as school/college authority thereby deducing whether the student is actually present in the class or not. Various activity trackers provide an Application Programming Interface (API) of their own to allow a third party application to access user's data from their server. Other data sets like surrounding noise level and location of the student have been used in combination with the step count of the student to produce real time inferences. This research paper gives an insight of how activity trackers can be utilised in the field of education thereby opening vast opportunities of data analysis and prediction in this field.","PeriodicalId":215344,"journal":{"name":"2015 IEEE Seventh International Conference on Technology for Education (T4E)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127011366","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}
{"title":"Adaptive and Gamified Learning Environment (AGLE)","authors":"V. Naik, V. Kamat","doi":"10.1109/T4E.2015.23","DOIUrl":"https://doi.org/10.1109/T4E.2015.23","url":null,"abstract":"The problems associated with teaching in a class of technical education are too many. The huge strength resulted from a large number of students being attracted to technical education field, a smaller teacher to student ratio due to unavailability of skilled instructors, requirement of completion of syllabi within stipulated time, gaps between industry demands and the proficiency of the graduates passing, are some among the most important ones. A lot of research has been dedicated towards finding the needs of the industry that absorb these graduates and it has been proved time and again that these students are not industry ready when they pass out. A major reason could be the inability of the faculty to cater to each student characteristics. Also there has been enough empirical evidence that it is difficult to maintain the interest and engagement of the students. This study is being taken up with the intention to devise a solution that can address both these problems i.e. meeting individual needs of the student as well as keeping away the disengagement and disinterest of the students. The system proposed here combines the advantages of Adaptive Learning Environment(ALE) and that of Gamification. Adaptive element is expected to help to match contents to the individual characteristics using User Modeling while Gamified aspect is perceived as a means to increase the involvement of the students. This study aims at measuring performance and finding the effect of adaptation and gamification on these two characteristics. Through our experimentation we intend to establish the fact that a setting that involves adaptation and gamification can surely have an impact in improving the performance as well as thecommitment of the learners.","PeriodicalId":215344,"journal":{"name":"2015 IEEE Seventh International Conference on Technology for Education (T4E)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115870397","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}
{"title":"Enabling Micro-Notes in Moodle for Educational Videos","authors":"K. Rajesh, R. Goudar, Viraj Kumar","doi":"10.1109/T4E.2015.24","DOIUrl":"https://doi.org/10.1109/T4E.2015.24","url":null,"abstract":"Micro-notes provide individual instructors with a simple, yet powerful mechanism to tailor educational videos to the needs of their own students. In this paper, we develop an easy-to-use, extension to Moodle (the popular and open-source Learning Management System), which permits instructors to create micro-notes for videos associated with their courses. These micro-notes can be viewed by students while watching videos online (via the Moodle platform), or can be downloaded along with videos for offline viewing (where they appear as sub-titles to videos). An instructor can also play videos with micro-notes in a classroom (blended learning). In this case, the video automatically pauses each time a micro-note is encountered. This permits the instructor to blend their own content into the video, and give students the opportunity to dwell on this additional material.","PeriodicalId":215344,"journal":{"name":"2015 IEEE Seventh International Conference on Technology for Education (T4E)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131550689","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}
Kavya Alse, M. Lahoti, Meenakshi Verma, Sridhar V. Iyer
{"title":"GATutor: A Guided Discovery Based Tutor for Designing Greedy Algorithm","authors":"Kavya Alse, M. Lahoti, Meenakshi Verma, Sridhar V. Iyer","doi":"10.1109/T4E.2015.26","DOIUrl":"https://doi.org/10.1109/T4E.2015.26","url":null,"abstract":"Greedy algorithms is an important class of algorithms. Teaching greedy algorithms is a complex task. Ensuring that students can design greedy algorithms for new problems is also complex. We have built a guided discovery based greedy algorithm tutor (GATutor), to teach the process of designing greedy algorithms. GATutor guides the student to discover the greedy algorithm for a few well-known problems, by asking two important questions -- i) what is the satisfying condition at each step? and ii) what is the selection criterion for the next item? As a result, the students not only learn the algorithms for the given problems, but also the process of designing greedy algorithms for new problems. We conducted a study to compare the greedy algorithm design abilities of the students who were trained with GATutor versus those who worked with traditional algorithm visualizations. The results indicate that students who worked with GATutor performed better in designing a greedy algorithm for a new problem. The students also said that their confidence in greedy algorithm design increased because of GATutor.","PeriodicalId":215344,"journal":{"name":"2015 IEEE Seventh International Conference on Technology for Education (T4E)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126128824","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}
{"title":"A Strategic Approach for Determining Students Specialization","authors":"A. Parkavi, N. Vetrivelan","doi":"10.1109/T4E.2015.9","DOIUrl":"https://doi.org/10.1109/T4E.2015.9","url":null,"abstract":"In certain scenarios, the subject chosen by the students for their projects, mini projects or seminars during their post-graduation courses might not be associated with their domain interest or specialization. In this paper, the authors have devised a methodology to identify and recommend a suitable specialization. This specialization will be recommended based on the conceptual and experimental skills of the students. The area of specialization is identified based on the past performance. In this paper, the authors have studied a certain population of students to validate the proposed strategy.","PeriodicalId":215344,"journal":{"name":"2015 IEEE Seventh International Conference on Technology for Education (T4E)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130196471","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}