Energy-Aware Streaming Multimedia Adaptation: An Educational Perspective

As mobile devices are getting more powerful and more affordable the use of online educational multimedia is also getting very prevalent. Limited battery power is nevertheless, a major restricting factor as streaming multimedia drains battery power quickly. Many battery efficient multimedia adaptation techniques have been proposed that achieve battery efficiency by lowering presentation quality of entire multimedia. Adaptation is usually done without considering any impact on the information contents of multimedia. In this paper, based on the results of an experimental study, we argue that without considering any negative impact on information contents of multimedia the adaptation may negatively impact the learning process. Some portions of the multimedia that require a higher visual quality for conveying learning information may lose their learning effectiveness in the adapted lowered quality. We report results of our experimental study that indicate that different parts of the same learning multimedia do not have same minimum acceptable quality. This strengthens the position that power-saving adaptation techniques for educational multimedia must be developed that lower the quality of multimedia based on the needs of its individual fragments for successfully conveying learning informatio

Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education

Students’ evaluation of teaching, for instance, through feedback surveys, constitutes an integral mechanism for quality assurance and enhancement of teaching and learning in higher education. These surveys usually comprise both the Likert scale and free-text responses. Since the discrete Likert scale responses are easy to analyze, they feature more prominently in survey analyses. However, the free-text responses often contain richer, detailed, and nuanced information with actionable insights. Mining these insights is more challenging, as it requires a higher degree of processing by human experts, making the process time-consuming and resource intensive. Consequently, the free-text analyses are often restricted in scale, scope, and impact. To address these issues, we propose a novel automated analysis framework for extracting actionable information from free-text responses to open-ended questions in student feedback questionnaires. By leveraging state-of-the-art supervised machine learning techniques and unsupervised clustering methods, we implemented our framework as a case study to analyze a large-scale dataset of 4400 open-ended responses to the National Student Survey (NSS) at a UK university. These analyses then led to the identification, design, implementation, and evaluation of a series of teaching and learning interventions over a two-year period. The highly encouraging results demonstrate our approach’s validity and broad (national and international) application potential—covering tertiary education, commercial training, and apprenticeship programs, etc., where textual feedback is collected to enhance the quality of teaching and learning

Predicting literature’s early impact with sentiment analysis in Twitter

Traditional bibliometric techniques gauge the impact of research through quantitative indices based on the citations data. However, due to the lag time involved in the citation-based indices, it may take years to comprehend the full impact of an article. This paper seeks to measure the early impact of research articles through the sentiments expressed in tweets about them. We claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. We used the SentiStrength tool and improved it by incorporating new opinion-bearing words into its sentiment lexicon pertaining to scientific domains. Then, we classified the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. Using positive and negative tweets as an independent variable, and the citation count as the dependent variable, linear regression analysis showed a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. Introducing an additional indicator to the regression model, i.e. ‘number of unique Twitter users’, improved the adjusted R-squared value of regression analysis in several disciplines. Overall, an encouraging positive correlation between tweet sentiments and citation counts showed that Twitter-based opinion may be exploited as a complementary predictor of literature’s early impact

The State of Altmetrics: A Tenth Anniversary Celebration

Altmetric’s mission is to help others understand the influence of research online.We collate what people are saying about published research in sources such as the mainstream media, policy documents, social networks, blogs, and other scholarly and non-scholarly forums to provide a more robust picture of the influence and reach of scholarly work. Altmetric works with some of the biggest publishers, funders, businesses and institutions around the world to deliver this data in an accessible and reliable format. Contents Altmetrics, Ten Years Later, Euan Adie (Altmetric (founder) & Overton) Reflections on Altmetrics, Gemma Derrick (University of Lancaster), Fereshteh Didegah (Karolinska Institutet & Simon Fraser University), Paul Groth (University of Amsterdam), Cameron Neylon (Curtin University), Jason Priem (Our Research), Shenmeng Xu (University of North Carolina at Chapel Hill), Zohreh Zahedi (Leiden University) Worldwide Awareness and Use of Altmetrics, Yin-Leng Theng (Nanyang Technological University) Leveraging Machine Learning on Altmetrics Big Data, Saeed-Ul Hassan (Information Technology University), Naif R. Aljohani (King Abdulaziz University), Timothy D. Bowman (Wayne State University) Altmetrics as Social-Spatial Sensors, Vanash M. Patel (West Hertfordshire Hospitals NHS Trust), Robin Haunschild (Max Planck Institute for Solid State Research), Lutz Bornmann (Administrative Headquarters of the Max Planck Society) Altmetric’s Fable of the Hare and the Tortoise, Mike Taylor (Digital Science) The Future of Altmetrics: A Community Vision, Liesa Ross (Altmetric), Stacy Konkiel (Altmetric

Saudi-KAU Coupled Global Climate Model: Description and Performance

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Learning analytics in mobile and ubiquitous learning environments

Learning analytics (LA) is one of the promising techniques that has been developed in recent times to effectively utilise the astonishing volume of student data available in higher education. Despite many difficulties in its widespread implementation, it has proved to be a very useful way to support failing learners. An important feature of the literature review of LA is that LA has not provided a significant benefit in terms of learner mobility to date since not much research has been carried out to determine the importance of LA in facilitating or enhancing the learning experience of mobile learners. Therefore, this paper describes the potential advantages of using LA techniques to enhance learning in mobile and ubiquitous learning environments from a theoretical perspective. Furthermore, we describe our simplified Mobile and Ubiquitous Learning Analytics Model (MULAM) for analysing mobile learners’ data which is based on Campbell and Oblinger’s five-step model of learning analytics. Finally, we answer the question why now might be the most suitable time to consider analysing mobile learners’ data

A comparison between mobile and ubiquitous learning from the perspective of human-computer interaction

In this paper, the notions of mobile learning and ubiquitous learning are compared from the viewpoint of the nature of interaction between learners and computers. This comparison leads to better understanding of their potential and the differences between these notions

Learning analytics and formative assessment to provide immediate detailed feedback using a student centered mobile dashboard

The ‘immediacy’ of feedback on academic performance is a common characteristic shared by both Learning Analytics (LA) and Formative Assessment (FA), and such immediacy could be facilitated by supporting the mobility of learners. However, there is little literature that investigates the significance of combining these two techniques. Therefore, this paper will discuss the analytical application called Quiz My Class Understanding (QMCU) which was purposely developed to investigate the significance of the combination between LA and FA techniques in order to provide students with immediate detailed feedback. Furthermore, it reports on a case study which reflects the role QMCU students ’ centered mobile dashboard in increasing the students’ engagement with the QMCU dashboard

The significance of trust to Twitter and its effect on the public/personal opinion divide: a case study

It has been documented that Twitter can be used as an essential method of communication between individualsand authorities during natural disasters such as floods, fire and earthquakes. However, this paper studied a real life incident that started in Twitter environment as user’s personal opinion, which was disseminated in the form of a tweet by user. In less than 48 hours, this unpopular personal opinion provoked criticism from the majority of Twitter users involved in this case study, which made it a very concerning public issue. The purpose of this study is to find out to what extent people trusted Twitter, in this case why the re-tweet rate increased so rapidly and why one tweet provoked wide criticism by involved users. Ultimately, the impact of a high number of followers on the distinction between private opinion and public offense leads to the conclusion that trust plays a tremendous role in social interactions in Twitter