Computational Intelligence for the Micro Learning

The developments of the Web technology and the mobile devices have blurred the time and space boundaries of people’s daily activities, which enable people to work, entertain, and learn through the mobile device at almost anytime and anywhere. Together with the life-long learning requirement, such technology developments give birth to a new learning style, micro learning. Micro learning aims to effectively utilise learners’ fragmented spare time and carry out personalised learning activities. However, the massive volume of users and the online learning resources force the micro learning system deployed in the context of enormous and ubiquitous data. Hence, manually managing the online resources or user information by traditional methods are no longer feasible. How to utilise computational intelligence based solutions to automatically managing and process different types of massive information is the biggest research challenge for realising the micro learning service. As a result, to facilitate the micro learning service in the big data era efficiently, we need an intelligent system to manage the online learning resources and carry out different analysis tasks. To this end, an intelligent micro learning system is designed in this thesis. The design of this system is based on the service logic of the micro learning service. The micro learning system consists of three intelligent modules: learning material pre-processing module, learning resource delivery module and the intelligent assistant module. The pre-processing module interprets the content of the raw online learning resources and extracts key information from each resource. The pre-processing step makes the online resources ready to be used by other intelligent components of the system. The learning resources delivery module aims to recommend personalised learning resources to the target user base on his/her implicit and explicit user profiles. The goal of the intelligent assistant module is to provide some evaluation or assessment services (such as student dropout rate prediction and final grade prediction) to the educational resource providers or instructors. The educational resource providers can further refine or modify the learning materials based on these assessment results

Building and Adaptive Learning Mechanism to Assist eLearning Students

Intelligent Tutoring System has been developed to provide an individualized learning environment in order to prompt learning interests and learning efficiency for online education. In this paper, based on the concept of intelligent tutoring system and educational product function, we establish a distance learning environment where students receive learning contents that best suit their needs. In this environment, we adopt clustering theory by utilizing SOM algorithm in the system and analyze the relationship between students’ personal background, interests and learning result. The workload of instructors is relieved and student’s learning ability and interests are considered in providing adequate learning contents which lead to more effective teaching effect and learning result. The proposed system is applied to the construction of an online 3D virtual museum

Machine learning approach to multicore data structures

In this study, a novel method for constructing self-aware data structures using online machine learning is proposed. This research introduced a novel category of data structures called Smart Data Structures, which continuously and automatically improve themselves to help simplify the complexity of manually modifying data structures for varied systems, applications, and workloads. This study also concluded that online machine learning is useful for autonomous data structure modification. For the online machine learning algorithm, I have proposed a reinforcement machine learning algorithm that benefits from the reward system and optimize the knobs accordingly. Online learning, in my opinion, offers a trustworthy and efficient framework for assessing intricate dynamic tradeoffs. Many of the possible difficulties that programmers may encounter in their daily work may be eliminated by using intelligent multicore data structures

Intelligent solution for automatic online exam monitoring

E-learning has shown significant growth in recent years due to its unavoidable benefits in unexpected situations such as the coronavirus disease 2019 (COVID-19) pandemic. Indeed, online exam is a very important component of an online learning program. It allows higher education institutions to assess student learning outcomes. However, cheating in exams is a widespread phenomenon worldwide, which creates several challenges in terms of integrity, reliability and security of online examinations. In this study, we propose a continuous authentication system for online exam. Our intelligent inference system based on machine learning algorithms and rules, detects continuously any inappropriate behavior in order to limit and prevent fraud. The proposed model includes several modules to enhance security, namely the registration module, the continuous students’ identity verification and control module, the live video stream and the end-to-end sessions recording

A solution-based intelligent tutoring system integrated with an online game-based formative assessment: development and evaluation

Nowadays, intelligent tutoring systems are considered an effective research tool for learning systems and problem-solving skill improvement. Nonetheless, such individualized systems may cause students to lose learning motivation when interaction and timely guidance are lacking. In order to address this problem, a solution-based intelligent tutoring system (SITS) is integrated with an online game-based formative assessment game called tic-tac-toe quiz for single-player (TRIS-Q-SP) for learning computer programming. This assessment game combines tic-tac-toe with online assessment, and the rules of tic-tac-toe are revised to stimulate students to use online formative assessment actively. Finally, an experimental study is devised to assess the success of SITS, and significant achievements are observed for the experimental group, besides enjoyment and positive opinions toward the TRIS-Q-SP. Therefore, the practical use of SITS is supported, as the results indicate considerable advantages for the experimental group over the control group. The findings also reveal that immediate elaborated feedback upon answering each question in TRIS-Q-SP is part of an optimal design

Attention-based High-order Feature Interactions to Enhance the Recommender System for Web-based Knowledge-Sharing Servic

Providing personalized online learning services has become a hot research topic. Online knowledge-sharing services represents a popular approach to enable learners to use fragmented spare time. User asks and answers questions in the platform, and the platform also recommends relevant questions to users based on their learning interested and context. However, in the big data era, information overload is a challenge, as both online learners and learning resources are embedded in data rich environment. Offering such web services requires an intelligent recommender system to automatically filter out irrelevant information, mine underling user preference, and distil latent information. Such a recommender system needs to be able to mine complex latent information, distinguish differences between users efficiently. In this study, we refine a recommender system of a prior work for web-based knowledge sharing. The system utilizes attention-based mechanisms and involves high-order feature interactions. Our experimental results show that the system outperforms known benchmarks and has great potential to be used for the web-based learning service

Design and Development of an Intelligent Online Personal Assistant in Social Learning Management Systems

Indiana University-Purdue University Indianapolis (IUPUI)Over the past decade, universities had a significant improvement in using online learning tools. A standard learning management system provides fundamental functionalities to satisfy the basic needs of its users. The new generation of learning management systems have introduced a novel system that provides social networking features. An unprecedented number of users use the social aspects of such platforms to create their profile, collaborate with other users, and find their desired career path. Nowadays there are many learning systems which provide learning materials, certificates, and course management systems. This allows us to utilize such information to help the students and the instructors in their academic life. The presented research work's primary goal is to focus on creating an intelligent personal assistant within the social learning systems. The proposed personal assistant has a human-like persona, learns about the users, and recommends useful and meaningful materials for them. The designed system offers a set of features for both institutions and members to achieve their goal within the learning system. It recommends jobs and friends for the users based on their profile. The proposed agent also prioritizes the messages and shows the most important message to the user. The developed software supports model-controller-view architecture and provides a set of RESTful APIs which allows the institutions to integrate the proposed intelligent agent with their learning system