Computer-supported Adaptive Management of Problem-based Learning

We propose an approach to a computer-supported adaptive management of problem-based learning (PBL) aimed at development of the higher-order thinking (HOT) skills in students. PBL is represented by the three-stage PBL process favoring adaptive management of intensive development of HOT skills in the students. The determined order of developing the HOT skills and solving the instructional problems is set. Adaptability of the management is provided by the dynamic assessments of the separate HOT skills, skill aggregates, one-skilled and multi-skilled instructional problems and personalized choice of instructional problems of suitable complexity for the students on the basis of the intermediate results of PBL. The learning results are evaluated by the coefficient of the HOT skill development. Adaptive management of the HOT skills development is supported by an adaptive management tool (AMT). Interactions an instructor and students with AMT are described. Keywords: Problem-Based Learning, Higher-Order thinking, Adaptive Management, Computer-suppor

Systematic Review of Adaptive Learning Research Designs, Context, Strategies, and Technologies From 2009 to 2018

This systematic review of research on adaptive learning used a strategic search process to synthesize research on adaptive learning based on publication trends, instructional context, research methodology components, research focus, adaptive strategies, and technologies. A total of 61 articles on adaptive learning were analyzed to describe the current state of research and identify gaps in the literature. Descriptive characteristics were recorded, including publication patterns, instructional context, and research methodology components. The count of adaptive learning articles published fluctuated across the decade and peaked in 2015. During this time, the largest concentration of adaptive learning articles appeared in Computers and Education. The majority of the studies occurred in higher education in Taiwan and the United States, with the highest concentration in the computer science discipline. The research focus, adaptive strategies, and adaptive technologies used in these studies were also reviewed. The research was aligned with various instructional design phases, with more studies examining design and development, and implementation and evaluation. For examining adaptive strategies, the authors examined both adaptive sources based on learner model and adaptive targets based on content and instructional model. Learning style was the most observed learner characteristic, while adaptive feedback and adaptive navigation were the most investigated adaptive targets. This study has implications for adaptive learning designers and future researchers regarding the gaps in adaptive learning research. Future studies might focus on the increasing availability and capacities of adaptive learning as a learning technology to assist individual learning and personalized growth

An adaptable and personalised e-learning system based on free web resources

A personalised and adaptive E-Learning system architecture is developed to provide a comprehensive learning environment for learners who cannot follow a conventional programme of study. The system extracts information from freely available resources on the Web, and taking into consideration the learners' background and requirements to design modules and a planner system to facilitate the learning process. The process is supported by the development of an ontology to optimise the in-formation extraction process. An application in the computer science field is used to evaluate the proposed system based on the IEEE/ACM Computing curriculum

Assessing the Usability of a Visual Tool for the definition of E-learning Processes

In this paper, we present a usability study aiming at assessing a visual language-based tool for developing adaptive e-learning processes. The tool implements the adaptive self-consistent learning object SET (ASCLO-S) visual language, a special case of flow diagrams, to be used by instructional designers to define classes of learners through stereotypes and to specify the more suited adaptive learning process for each class of learners. The usability study is based on the combined use of two techniques: a questionnaire-based survey and an empirical analysis. The survey has been used to achieve feedbacks from the subjects' point of view. In particular, it has been useful to capture the perceived usability of the subjects. The outcomes show that both the proposed visual notation and the system prototype are suitable for instructional designers with or without experience on the computer usage and on tools for defining e-learning processes. This result is further confirmed by the empirical analysis we carried out by analysing the correlation between the effort to develop adaptive e-learning processes and some measures suitable defined for those processes. Indeed, the empirical analysis revealed that the effort required to model e-learning processes is not influenced by the experience of the instructional designer with the use of e-learning tools, but it only depends on the size of the developed process

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

Providing Intelligent and Adaptive Support in Concept Map-based Learning Environments

abstract: Concept maps are commonly used knowledge visualization tools and have been shown to have a positive impact on learning. The main drawbacks of concept mapping are the requirement of training, and lack of feedback support. Thus, prior research has attempted to provide support and feedback in concept mapping, such as by developing computer-based concept mapping tools, offering starting templates and navigational supports, as well as providing automated feedback. Although these approaches have achieved promising results, there are still challenges that remain to be solved. For example, there is a need to create a concept mapping system that reduces the extraneous effort of editing a concept map while encouraging more cognitively beneficial behaviors. Also, there is little understanding of the cognitive process during concept mapping. What’s more, current feedback mechanisms in concept mapping only focus on the outcome of the map, instead of the learning process. This thesis work strives to solve the fundamental research question: How to leverage computer technologies to intelligently support concept mapping to promote meaningful learning? To approach this research question, I first present an intelligent concept mapping system, MindDot, that supports concept mapping via innovative integration of two features, hyperlink navigation, and expert template. The system reduces the effort of creating and modifying concept maps while encouraging beneficial activities such as comparing related concepts and establishing relationships among them. I then present the comparative strategy metric that modes student learning by evaluating behavioral patterns and learning strategies. Lastly, I develop an adaptive feedback system that provides immediate diagnostic feedback in response to both the key learning behaviors during concept mapping and the correctness and completeness of the created maps. Empirical evaluations indicated that the integrated navigational and template support in MindDot fostered effective learning behaviors and facilitating learning achievements. The comparative strategy model was shown to be highly representative of learning characteristics such as motivation, engagement, misconceptions, and predicted learning results. The feedback tutor also demonstrated positive impacts on supporting learning and assisting the development of effective learning strategies that prepare learners for future learning. This dissertation contributes to the field of supporting concept mapping with designs of technological affordances, a process-based student model, an adaptive feedback tutor, empirical evaluations of these proposed innovations, and implications for future support in concept mapping.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Intrusion Detection System using Bayesian Network Modeling

Computer Network Security has become a critical and important issue due to ever increasing cyber-crimes. Cybercrimes are spanning from simple piracy crimes to information theft in international terrorism. Defence security agencies and other militarily related organizations are highly concerned about the confidentiality and access control of the stored data. Therefore, it is really important to investigate on Intrusion Detection System (IDS) to detect and prevent cybercrimes to protect these systems. This research proposes a novel distributed IDS to detect and prevent attacks such as denial service, probes, user to root and remote to user attacks. In this work, we propose an IDS based on Bayesian network classification modelling technique. Bayesian networks are popular for adaptive learning, modelling diversity network traffic data for meaningful classification details. The proposed model has an anomaly based IDS with an adaptive learning process. Therefore, Bayesian networks have been applied to build a robust and accurate IDS. The proposed IDS has been evaluated against the KDD DAPRA dataset which was designed for network IDS evaluation. The research methodology consists of four different Bayesian networks as classification models, where each of these classifier models are interconnected and communicated to predict on incoming network traffic data. Each designed Bayesian network model is capable of detecting a major category of attack such as denial of service (DoS). However, all four Bayesian networks work together to pass the information of the classification model to calibrate the IDS system. The proposed IDS shows the ability of detecting novel attacks by continuing learning with different datasets. The testing dataset constructed by sampling the original KDD dataset to contain balance number of attacks and normal connections. The experiments show that the proposed system is effective in detecting attacks in the test dataset and is highly accurate in detecting all major attacks recorded in DARPA dataset. The proposed IDS consists with a promising approach for anomaly based intrusion detection in distributed systems. Furthermore, the practical implementation of the proposed IDS system can be utilized to train and detect attacks in live network traffi

AEINS: Interactive Narrative Role in Fostering Character Education

Promoting ethical, responsible, and caring young people is a perennial aim of education. Efforts have been done to find other teaching ways other than traditional ones such as games and role play. Narrative-based computer games have found their way as engaging learning platforms that allow collaboration of humans and computers in the creation of innovative experiences. In this paper, we focus on the design of an adaptive, interactive narrative model that makes use of a student model to provide an individualized story-path and an individualized learning process. In other words, we aim to have strong learning objectives underpinned by effective story telling. The adaptive narrative model has been deployed in the educational game environment, AEINS, along with the use of the Socratic Method and pedagogical agents to help teaching in the ethics domain. Evaluation results indicate the usefulness of the design and provide evidence on the development of moral reasoning and the transfer of moral virtues to its users