Smart Learning Environment: Paradigm Shift for Online Learning

Online learning has always been influenced by advanced technology. The role of online learning is expected not only for delivering contents to massive learners anywhere and anytime but also for promoting successful learning for the learners. Consequently, this emerged role has introduced the concept of smart learning environment. More specifically, smart learning environment is developed to promote personalized learning for learners. Personalized learning focuses on individual learner and provides appropriate feedback individually. Currently, the advances of modern technologies and intelligence data analytics have brought the idea of smart learning environment into realization. Machine learning techniques are generally applied to analyze real-time dynamic learner behavior and provide the appropriate response to the right learner. In this chapter, the evolution of online learning environment from different points of technological overviews is first introduced. Next, the concepts of personalized learning and smart learning environment are explained. Then, the essential components of smart learning environment are presented including learner classification and intervention feedback. Learner classification is to understand different learners. Intervention feedback is to provide an individual response appropriately. Additionally, some machine learning techniques widely used in smart learning environment in order to perform smart classification and response are briefly explained

Collaborative Educational Game for Thai Primary School Students

English language, Mathematics and Sciencefor life are mandatory subjects for Thai students to finish theirprimary school studies. Regarding the unsatisfied result of theannual assessment, there are many students fail those subjectsevery year. This paper thus proposes the educational computergame to enhance learning of English language, Mathematics andSciences subjects. The proposed game employs the concept ofcollaborative learning integrated into the game to promote thebetter understanding of contents and the familiarization of teamwork experience, while the players are still filled with the joy andthe challenge. The proposed game is designed as a multi-playersonline game. All players compete among each other to be a leaderand conduct the game along with help from team members toachieve the goal. The developed game is evaluated with 2 aspectsincluding the learning efficiency and the satisfaction of students.The empirical study is conducted with 100 students from 3different primary schools in Chiang Rai, Thailand. These studentsare divided into 2 groups including the group playing gameindividually and collaboratively respectively. The first group has25 students while the second group has 15 groups with 5 studentsper each. The results reveal that, the students playing gamecollaboratively can achieve higher learning efficiency than thestudents playing game individually. Moreover, the collaborativegame obtains Good satisfaction level by the students

Arts and Science of Digital Mental Health Support

Globally, over 600 million people are affected by mental health-related issues, according to the recent statistics. Similar to the global scenario, these issues are the single source of disease burden in the UK as well. On the bright side, the boom in the mobile application or `app' market and the paradigm shift in the global disease burden has promoted a growing interest for mental health-related apps. This paper critically explores the mobile application for mental health support to understand what apps are available in the market and their merits and capabilities to meet the need of users' demand. This paper also provides a recommendation for future directions

Classification Method for Thai Elderly People Based on Controllability of Sugar Consumption

Nowadays, the number of Thai elders is rapidly increasing among world elderly population, how to keep their health is a major concern. Cardiovascular Diseases (CVDs) which are severe diseases for Thai have higher mortality than cancers, and elderly people have a higher possibility to predispose CVDs. Hence, the risk factors for CVDs should be addressed. Obesity, as one of the risk factors of CVDs, seriously affects Thai elders' wellbeing; excessive sugar consumption is a way leading to overweight and obesity. The amount of consumed sugar by Thai is much higher than the standard sugar consumption, and it also could cause many other diseases. Therefore, this paper proposes a classification method for the elderly group who have the potential to control their blood sugar in order to prevent them from sugar overconsumption. This paper explored machine learning algorithms to find an appropriate classification method for elderly data. Artificial neuron network and K-nearest neighbors are applied for classifying elderly groups. Glycated hemoglobin (HbA1c) and fasting plasma glucose (FPG) are the noninvasive measurements of evaluating blood sugar, based on the two measurements, the 242 data from 121 elderly people are divided into two groups which are controllable group and uncontrollable group. The result indicates that the artificial neuron network is more suitable for the dataset with 70.59% accuracy as compared to the accuracy of K-nearest neighbors

Abstract Of Collaborative Learning Team: Cooperation Leader Identification by Using Information Theory Of Collaborative Learning Team: Cooperation Leader Identification by Using Information Theory

This paper proposes an approach for identifying the cooperation leader in a collaborative learning team. The cooperation leader is the member potentially obtaining the effectively cooperation from other team members. The concept of information theory, the measure of uncertainty, is applied in this paper in order to identify the cooperation leader candidate. Because the cooperation leader is likely to have equally intensive connections with other members, the cooperation leader thus should have the maximum of the measure of uncertainty. That means it is difficult to predict that which members among all other members will respond to the cooperation leader, because all of them are likely to have equal chances to response. Based on the proposed assumptions, the experiments with two pilot studies are conducted. The leadership perceptions observed from team members are compared with the computed measure of uncertainty. The results show that the measure of uncertainty will be able to represent the leadership perceptions from team members. However, more experiment for the future work is still required

Individual Attribute Selection Using Information Gain Based Distance for Group Classification of Elderly People With Hypertension

Attribute selection is the process of selecting relevant attributes being used in model construction to enhance model accuracy. For general medical oriented classification applications, classical attribute selection methods principally select common attributes in the dataset for all individuals. The idea of using individual attributes is proposed in this study to represent the difference among individuals for self-diagnosis. Consequently, this study proposes a new attribute selection method, called information gain based distance (IGD), for individual attribute selection, which represents an individual’s health condition differently and can be used for effective classification. The proposed method combines the concept of information gain and objective distance to select individual attributes affecting classification. The IGD method is expected to provide higher classification performance than classical attribute selection methods. To assess the performance of the IGD method, classification accuracy between data with classical attribute selections and with the IGD method is compared. The case study is conducted with 971 secondary data used for group classification of elderly people with hypertension. The classification result of different classifiers was compared, including K-nearest neighbors, neural network, and naive Bayes. The comparison revealed that the classification of data with the IGD attribute selection method provided an average classification accuracy of 98.73%. In comparison, those classifications of data with classical attribute selection methods provided 62.99%, 62.99%, 62.62%, and 62.85% for information gain, Gini index, chi-squared, and decision tree, respectively. The results showed that data classification with the IGD method provided higher performance than those with the classical attribute selection methods