Evaluation of resource creations accuracy by using sentiment analysis

Opinionated texts in various social media can be further processed using sentiment analysis to generate a sentiment polarity reading. This reading shows the general public view toward a product for both developer and user. However, the task is difficult due to the dynamic changes in these texts. The purpose of this paper is to evaluate the capability of existing dictionaries (resource creations) to analyse the opinionated comments from an online learning video

Predicting Students’ Course Performance Based on Learners’ Characteristics via Fuzzy Modelling Approach

Frequent assessment allows instructors to ensure students have met the course learning objectives. Due to lack of instructor-student interaction, most of the assessment feedbacks and early interventions are not carried out in the large class size. This study is to proposes a new way of assessing student course performance using a fuzzy modeling approach. The typical steps in designing a fuzzy expert system include specifying the problem, determining linguistic variables, defining fuzzy sets as well as obtaining and constructing fuzzy rules is deployed. An educational expert is interviewed to define the relationship between the factors and student course performance. These steps help to determine the range of fuzzy sets and fuzzy rules in fuzzy reasoning. After the fuzzy assessing system has been built, it is used to compute the course performances of the students. The subject expert is asked to validate and verify system performance. Findings show that the developed system provides a faster and more effective way for instructors to assess the course performances of students in large class sizes.  However, in this study, the system is developed based on 150 historical student data and only a total of six factors related to course performance are considered. It is expected that considering more historical student data and adding more factors as the variables help to increase the accuracy of the system

FORECASTING CRUDE OIL PRICE USING ARIMA AND FACEBOOK PROPHET WITHI MACHINE LEARNING

Oil price forecasting has received a great deal of attention from practitioners and researchers alike, but it remains a difficult topic because of its dependency on a variety of factors, including the economic cycle, international relations, geopolitics, and so on. Forecasting the price of oil is a difficult but gratifying task. Motivated by this issue, we present a robust model for accurate crude oil price forecasting using ARIMA and PROPHET models based on machine learning technique to produce a reliable weekly and monthly crude oil price predictions. We apply the Savitzky Golay smoothing filter to get a better denoising performance for our forecast models. For model evaluation, we apply cross validation with sliding windows on both models and compares the performances using RMSE and MAPE. The results shows that the ARIMA- based machine learning approach performs better as compared to the PROPHET model for both one-week and one-month forecast ahead intervals

A Unified Latent Variable Model for Contrastive Opinion Mining

Ebuka IBEKE, Chenghua LIN , Adam WYNER, Mohamad Hardyman BARAW

Is Facebook PROPHET superior than hybrid ARIMA model to forecast crude oil price?

Oil price forecasting has received a great deal of attention from practitioners and researchers alike, but it remains a difficult topic because of its dependency on a variety of factors, including the economic cycle, international relations, and geopolitics. Forecasting the price of oil is a difficult but gratifying task. Motivated by this issue, we present a robust model for accurate crude oil price forecasting using ARIMA and Prophet models based on machine learning technique to produce a reliable weekly and monthly crude oil price predictions. We apply the Savitzky–Golay smoothing filter to get a better denoising performance for our forecast models. For model evaluation, we apply cross validation with sliding windows on both models and compares the performances using RMSE and MAPE. The results show that the ARIMA-based machine learning approach performs better as compared to the Prophet model for both one-week and one-month forecast ahead intervals

Predicting Students’ Course Performance Based on Learners’ Characteristics via Fuzzy Modelling Approach

Frequent assessment allows instructors to ensure students have met the course learning objectives. Due to lack of instructor-student interaction, most of the assessment feedbacks and early interventions are not carried out in the large class size. This study is to proposes a new way of assessing student course performance using a fuzzy modeling approach. The typical steps in designing a fuzzy expert system include specifying the problem, determining linguistic variables, defining fuzzy sets as well as obtaining and constructing fuzzy rules is deployed. An educational expert is interviewed to define the relationship between the factors and student course performance. These steps help to determine the range of fuzzy sets and fuzzy rules in fuzzy reasoning. After the fuzzy assessing system has been built, it is used to compute the course performances of the students. The subject expert is asked to validate and verify system performance. Findings show that the developed system provides a faster and more effective way for instructors to assess the course performances of students in large class sizes. However, in this study, the system is developed based on 150 historical student data and only a total of six factors related to course performance are considered. It is expected that considering more historical student data and adding more factors as the variables help to increase the accuracy of the system

Usability evaluation of a virtual reality smartphone app for a living museum

This paper elaborates the empirical evidence of a usability evaluation of a VR and non-VR virtual tour application for a living museum. The System Usability Scale (SUS) was used in between participants experiments (Group 1: non-VR ver-sion and Group 2: VR version) with 40 participants. The results show that the mean scores of all components for the VR version are higher compared to the non-VR version, overall SUS score (72.10 vs 68.10), usability score (75.50 vs 71.70), and learnability (58.40 vs 57.00). Further analysis using a two-tailed independent t test showed no difference between the non-VR and VR versions. Additionally, no significant difference was observed between the groups in the context of gender, nationality, and prior experience (other VR tour applications) for overall SUS score, usability score, and learnability score. Α two-tailed independent t test indicated no significant difference in the usability score between participants with VR experi-ence and no VR experience. However, a significant difference was found between participants with VR experience and no VR experience for both SUS score (t(38) = 2.17, p = 0.037) and learnability score (t(38) = 2.40, p = 0.021). The independent t test results indicated a significant difference between participant with and without previous visits to SCV for the usability score (t(38) = −2.31, p = 0.027), while there was no significant differences observed in other components. It can be concluded that both versions passed based on the SUS score. However, the sub-scale usability and learnability scores indicated some usability issue

Using entropy to measure text readability in Bahasa Malaysia for year one students.

Text readability is essential for effective learning and communication, especially for beginner readers. However, there are no known measures to calculate the readability of Bahasa Malaysia, the national language of Malaysia. This research proposes a new method based on entropy, a measure of information and uncertainty, to assess the readability of Bahasa Malaysia texts for Year One students. An experiment was conducted with six Year One students to determine the relationship between entropy and readability. The results indicated a positive correlation, suggesting that higher entropy values corresponded with lower readability for this age group. This study also revealed the need for beginner readers to focus on the text difficulty level to enhance learning

LEARNING PROGRAMMING USING VISUALISATION- AN ANALYSIS OF LEARNER EXPERIENCES

Computational thinking and problem-solving are crucial skills of twenty-first-century education. The abstractness and problem-solving nature of programming is a challenge for novice learners. We experimented with an online free visualisation tool called Python Tutor for Object-Oriented C++ programming to improve the learning of visualisation of abstract concepts, problem-solving and computational thinking. In this study, we engaged two classes of undergraduate students. To investigate the learning effects of the visualisation tool in learning, Class One (C1) was treated as an experimental group, and Class Two (C2) was a control group. The same topics were taught to both groups. The experimental group received an opportunity to use the selected tool as they learned the taught content. At the end of two sessions, a quiz was distributed to both groups. Next, C1 was treated as the control group, and C2 became the experimental group. The same topics were thought to both groups. At the end of the two sessions, a second quiz was given to both groups and scores were recorded. To gauge all participants’ problem-solving and computational thinking skills as a whole, we collected data at the beginning and the end of the semester using a Computational Thinking Scales (CTS) and Problem-Solving Test. Findings indicate that the selected visualisation tool helped participants understand and solve ill-defined problems, a critical skill in learning Programming