Social Media in E-Learning: An Empirical Analysis among Students and Academicians

This study investigated the factors that might affectthe use of social media in e-learning, particularly targeting theuse of Facebook, Twitter and YouTube. Questionnaire designwas based on Push-Pull-Mooring framework, and distributed tostudents and academicians in local universities (N = 455). Fivesignificant predictors were found, viz. E-learning Perception,Convenience, Academic Reasons, Ease of Use and SocialNetworking. A further analysis revealed younger respondentswere more enthusiastic in using social media in e-learning,particularly for Academic Reasons, Social Networking and Easeof Use. The findings indicate that students and academicians areopen to the idea of using social media in e-learning, moreapparent among the younger ones. It is believed that integrationof social media in e-learning will enhance communication andcollaboration among students and academicians

Collaborative learning via CiA - collaborators in action

The present study investigated the effect(s) of using a collaborative tool, aptly named Collaborators in Action (CiA) in improving students' collaborative learning. CiA was developed based on the Social Media Acceptance Model (SMAM), which consists of four main predictors, namely Communication Functionality (i.e. functions supporting communication process), Effort (i.e. ease of use), Performance (i.e. usefulness of CiA) and Self (i.e. skill set and enjoyment). CiA has several core components, such as Collaborative Activities that enable the lecturer to administer various collaborative tasks to the students, Tracking and Monitoring that allows the students' collaboration level to be monitored and Sentiment Analysis, which provides the general polarity of the students' sentiment for a particular topic or activity. The tool was built and customized for the Probability and Statistics, that is, a core course that require a certain number of collaborative tasks to be carried out. Both pre-test and post-test surveys were administered among 33 undergraduate students, with the students using CiA as part of their learning for the second half of the semester (i.e. approximately six weeks). The pre-test questionnaire was given to the students at the beginning of the experiment and they were asked to provide their opinions based on the current practice of learning. At the end of the sixth week, the posttest questionnaire was administered. Paired-sample t-test revealed the students to be more receptive in using CiA in collaborative learning. A further in-depth analysis of the survey instruments and CiA is yet to be carried out, and therefore more results will be provided at a later stage

A one-mode-for-all predictor for text messaging

This paper discusses the enhancements made on the current mobile phone messaging software, namely the predictive text entry. In addition, the application also has a facility to abbreviate any unabbreviated words that exist in the dictionary, so that the message length can be reduced. The application was tested in a computer-simulated mobile environment and the results of the tests are presented here. These additional features will potentially enable users to send messages at a reduced length and thus reduce the cost of sending messages. Moreover, users who are not adept in using the abbreviations can now do so with features made available on their mobile phones. It is believed that these additional features will also encourage more users to use the predictive software as well as further improve users’ messaging satisfaction

Social Media Sentiment Analysis of Thermal Engineering Students for Continuous Quality Improvement in Engineering Education / Wandeep Kaur ...[et al.]

In an academic institution, deciphering the opinions of students is the key that ensures the institution continues to strive within the education industry. Extracting implicit information from student opinions are vital in ensuring the standard of education continuously improves, ultimately leading to student retention and increase number of student intake within the institution. Sentiment analysis is a field of study that is interested in extracting sentiments from opinions extracted from written text. These techniques determine if an opinion is penchant towards positivity or negativity. The main aim of this paper is to conduct a preliminary analysis on the opinions of students taking Thermal Engineering (MEC551) from Universiti Teknologi Mara (UiTM) with regard to course tools. Data collected from Facebook was subjected to cleaning and pre-processing. A supervised machine learning algorithm was employed for sentiment classification purpose which was implemented using Rapid Miner. Algorithms were compared and results indicate Support Vector Machine (93.6%) outperformed Naïve Bayes (90.1%) and K-Nearest Neighbour (90.2%) in terms of accuracy and was able to correctly classify the text accordingly. This in return indicates students were very much interested in being able to interact and discuss on questions and queries via Facebook as well as address some fears they had related to exams and assignments seamlessly with their classmates as well as lecturer

Low cost redesign of coil assemblies for ergonomic improvements / Loo Huck-Soo, Paul H.P. Yeow and Vimala Balakrishnan

This paper investigated into an alternative approach to solving cooling problems in two typical shopping complexes in Malaysia. For each case, an investigation was initially conducted to capture a general idea of poor indoor cooling suffered by the business operators and patrons. Ergonomics methods such as unstructured interviews and direct observations (DOs) were applied to obtain information on the major complaints. Visual inspections into the air handlers and the air ducts were also conducted. Subjective assessments (SAs) were utilized to test the response of the business operators. Cost accounting figures, both current and archival data pertaining to air handler maintenance service were retrieved and analysed. Ergonomic interventions were implemented by incorporating applied basic sciences into the rectification work. The original design specifications were modified to produce those for the new make. After new installations were in place, follow-up studies using similar methods (i.e. DOs, SAs, current and archival data) were conducted to assess the effectiveness of the interventions. It was found that the installations were cost effective. The new designs improved human comfort through effective heat removal from the air conditioned space. There were also cost savings in the maintenance of the new coil assemblie

A comparative analysis of detection mechanisms for emotion detection

This paper compared the performance of emotion detection mechanisms using dataset crawled from Facebook diabetes support group pages. To be specific, string-based Multinomial Naïve Bayes algorithm, NRC Emotion Lexicon (Emolex) and Indico API were used to detect five emotions present in 2475 Facebook posts, namely, fear, joy, sad, anger and surprise. Both accuracy and F-score measures were used to assess the effectiveness of the algorithms in detecting the emotions. Findings indicate string-based Multinomial Naïve Bayes to outperform both Emolex (i.e. 82% vs. 78%) and Indico API (i.e. 82% vs. 50%). Further analysis also revealed emotions such as joy, fear and sadness to be of the highest frequencies for the diabetes community. Implications of the findings and emotions detected are further discussed in this pape

A comparative analysis of detection mechanisms for emotion detection

This paper compared the performance of emotion detection mechanisms using dataset crawled from Facebook diabetes support group pages. To be specific, string-based Multinomial Naïve Bayes algorithm, NRC Emotion Lexicon (Emolex) and Indico API were used to detect five emotions present in 2475 Facebook posts, namely, fear, joy, sad, anger and surprise. Both accuracy and F-score measures were used to assess the effectiveness of the algorithms in detecting the emotions. Findings indicate string-based Multinomial Naïve Bayes to outperform both Emolex (i.e. 82% vs. 78%) and Indico API (i.e. 82% vs. 50%). Further analysis also revealed emotions such as joy, fear and sadness to be of the highest frequencies for the diabetes community. Implications of the findings and emotions detected are further discussed in this pape

Ringed Seal Search for Global Optimization via a Sensitive Search Model.

The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behav-ior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emit-ted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and valida-tions were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of conver-gence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global optimization problems

Ergonomic Study On Input-Output Devices For Mobile Computing

The study focused on keypad design, screen design, text entry, mobile phone design and health effeects

Unraveling the underlying factors SCulPT-ing cyberbullying behaviours among Malaysian young adults

Investigating factors that drive people to cyberbully is critical, so that early interventions could be administered. Most cyberbullying studies were conducted in Western countries, with only a few focusing on Asia. This current study was undertaken to (i) design a cyberbullying behavioral model, and (ii) determine the predictors for intention to cyberbully. The first objective was achieved by integrating Sociocultural-Psychology-Technology factors into a model, aptly named SculPT, which was subsequently used to address the second objective. Additionally, a further analysis was conducted to identify specific influential sub-factors based on SculPT. The methodology involves a mixed method approach, namely focus groups and online questionnaire surveys. Three hundred and ninety nine respondents between 17 and 36 years old (M = 22.03, SD = 2.77) were recruited. Path modeling analysis revealed SculPT to predict approximately 83% of cyberbullying perpetration. All the predictors were found to have significant direct effects on intention to cyberbully, with Sociocultural having the strongest impact, followed by Technology and Psychology. A more in-depth analysis revealed five sub-factors to significantly predict cyberbullying intentions, namely Social Influence and Social Acceptability (Sociocultural), Availability and Ease of Use (Technology), and Entertainment (Psychology). Results generally concur with previous studies, and comparisons were made where appropriate. We conclude that cyberbullying prevalence still exists among young adults in the country, and with the revelation of factors behind cyberbullying, respective authorities could focus on the identified factors to help mitigate cyberbullying behaviors