Improvement of the Usability of Online Mentoring Website

The purpose of this study is to improve the usability of the current online mentoring website by deriving what should be improved through assessment and reflecting it to system improvement. The related data such as search log and Think Aloud were collected from user groups (9 users in total), and usability was tested according to the predefined test procedures. The collected data were analyzed, using quantitative methods. In terms of search log, the related items including effectiveness, efficiency, satisfaction and error were quantified according to usability testing standards. Then, descriptive statistics was performed. According to usability comparison before and after system improvement, it has mostly improved such as improved effectiveness (increase by 15 points), better efficiency (reduction by 41 seconds), increase in satisfaction (by 8 points) and decrease in error frequency (decrease by 1.2 times). Usability testing should be viewed as a process, not outcome itself. Therefore, it could be used during system prototype in addition to the current system and useful in system improvement

Development of a Mind Map System Integrating Full Moodle Function

An LMS is the most basic infrastructure used in e-learning. Various types of LMSs are used, covering open source and commercial software. Moodle is one of the most widely used LMSs today. Moodle has many features that are educationally useful because of its long history and contributions of community. Moodle’s wide variety of functions are sometimes criticized as being too complicated to use. It is necessary to develop an LMS with various functions inside a simple and user-friendly GUI. In this paper, a Moodle GUI has been improved by mapping the main functions of a Moodle course to OKMindmap, which is an open service that runs on the Web, so that learners and teachers can use LMS services in a simple and convenient way. The developed results are demonstrated by applying actual class scenarios with which course activities, supplementary resources, and additional guide information was successfully integrated in a mind map

Exploiting General-Purpose In-Game Behaviours to Predict Players Churn in Gameful Systems

The value of a game is assessed by measuring the intensity of the level of activity of its players. No matter how thoroughly though the design is, the litmus test is whether players keep using it or not. To reduce the number of abandoning players, it is important to detect in time the subjects at risk. In the literature, many works are targeting this issue. However, the main focus has been on entertainment games, from which articulated indicators of in-game behaviors can be extracted. Those features tend to be context-specific and, even when they are not, they are proper of full-featured games, and thus, impossible to adapt to other systems such as games with a purpose and gamified apps. In this preliminary work, we fed to an Artificial Neural Network general-purpose in-game behaviors, such as participation data, to predict when a player will definitively leave the game. Moreover, we study the appropriate amount of information, in terms of players’ history, that should be considered when predicting players’ churn. Our use case study is an on-the-field long-lasting persuasive gameful system

Movie Recommendation Service Based on Preference Correlation Coefficient of Audience in Smart Environment

Recommendation system has been more and more popular recent years. It can help people make decisions easily, and is used in many popular applications include movies, music, news, books, research articles, search queries, social tags, and products in general. Smart homes also get enormous attention in the last decade, due to the important applications like health, energy and security. Different techniques and approaches have been devised by the researchers to make the smart home more efficient and effective. In this paper, we propose the movie recommendation service based on preference correlation coefficient of audience in smart environment, which will lead to the entertainment convenient in smart environment

A Study on Machine Learning Based Light Weight Authentication Vector

Artificial Intelligence area has been rapidly advanced around the global companies such as Google, Amazon, IBM and so on. In addition, it is anticipated to facilitate the innovation in a variety of industries in the future. AI provides us with convenience in our lives, on the other hand, the valuable information on the subjects that utilize this has the potential to be exposed at anytime and anywhere. In the next advancement of AI area, the technical developments of the new security are required other than the existing methods. Generation and validation methods of light-weight authentication vector are suggested in this study to be used in many areas as an expanded security function. Upon the results of the capacity analysis, it was verified that efficient and safe security function could be performed using the existing machine learning algorithm. Authentication vector is designed to insert the encrypted data as variable according to the change of time. The security function was performed by comparing coordinate distance values within the authentication vector, and the internal structure was verified to optimize the performance cost required for data reverse search

A Design and Development of the Learning Contents Management based on the Personalized Online Learning

Teaching-learning methods are undergoing rapid transformation in terms of new information and communication technology and in accordance with onset of the 4th Industrial Revolution. The educational environment is being transformed into various forms, with examples being found not only in the existing traditional educational environment, but also in online education and blended learning. Existing online learning (LMS, LCMS) is offered in a limited contents transmission online educational environment, and has been limited but the level of support offered to a learner’s personalized learning. This study will overview existing flexible model of contents, suggest possible problems, and attempt to solve these problems. LCMS was designed and realized based on the open source Moodle platform, offering personalized contents to learners. LCMS is composed of the following 3 functions: contents registration of metadata inputted by administrator; search functionality for personalized learner contents; and personalized contents automatically being recommended to learners. As a result of the research, we made online learning environment that can provide customized learning recommendation and self - directed learning by increasing the continuity and efficiency of learning by automatically providing customized online contents to learners. Through this study, the learning of students promises to be effectively initiated by being based on available LCMS functions related to personalized educational contents in online education

Evaluation of Packet Tracer Application Effectiveness in Computer Design Networking Subject

The dynamics of technological progress in various fields indirectly affect the world of education. One of the indications can be seen in the use of media as a learning tool. This study aims; 1) to investigate the effectiveness of the packet tracer learning media application, 2) to investigate the responses of the students to such applications, 3) to determine the appropriate media used in a learning process.  The research is carried out in the subject of Computer Design Networking. The evaluation research is employed by using a mixed method of qualitative and quantitative approach. The quantitative analysis involved 58 students, while the qualitative analysis involved one productive teacher, one curriculum representative, and four students. Data are analysed by using quantitative and qualitative data analysis techniques. The results show that, the effectiveness of learning media with packet tracer is high i.e., 82.76%, which covers three aspects; software engineering, learning aspects, and display aspects. This means that this packet tracer learning media can be applied as a solution to the limitation of facilities and infrastructures of network practices by considering certain learning situations and conditions. The results indicate that this application is very useful to deal with the high cost of practising tools. It makes the students more enthusiastic and motivated during the learning process. Hence, by implementing this learning method, the interaction and the learning outcomes of the student can be increased

Characterising Players of a Cube Puzzle Game with a Two-level Bag of Words

Ponencia presentada en UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, Utrecht (Netherlands), June 21 - 25, 2021This work explores an unsupervised approach for modelling players of a 2D cube puzzle game with the ultimate goal of customising the game for particular players based solely on their interaction data. To that end, user interactions when solving puzzles are coded as images. Then, a feature embedding is learned for each puzzle with a convolutional network trained to regress the players’ comple tion effort in terms of time and number of clicks. Next, the known bag-of-words technique is used at two levels. First, sets of puzzles are represented using the puzzle feature embeddings as the input space. Second, the resulting first-level histograms are used as input space for characterising players. As a result, new players can be characterised in terms of the resulting second-level histograms. Preliminary results indicate that the approach is effective for char acterising players in terms of performance. It is also tentatively observed that other personal perceptions and preferences, beyond performance, are somehow implicitly captured from behavioural data

Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

Game player modeling is a paradigm of computational models to exploit players’ behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game. Player behavior focuses on dynamic and static information gathered at the time of gameplay. Player experience concerns the association of the human player during gameplay, which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings. In this paper, player experience modeling is studied based on the board puzzle game “Candy Crush Saga” using cognitive data of players accessed by physiological and peripheral devices. Long Short-Term Memory-based Deep Neural Network (LSTM-DNN) is used to predict players’ effective states in terms of valence, arousal, dominance, and liking by employing the concept of transfer learning. Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems. The homogeneous transfer learning approach has not been implemented in the game domain before, and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’ engagement. Relevant not only from a player’s point of view, it is also a benchmark study for game developers who have been facing problems of “cold start” for innovative games that strengthen the game industrial economy