Harmony and Technology Enhanced Learning

New technologies offer rich opportunities to support education in harmony. In this chapter we consider theoretical perspectives and underlying principles behind technologies for learning and teaching harmony. Such perspectives help in matching existing and future technologies to educational purposes, and to inspire the creative re-appropriation of technologies

Human-AI Collaboration for Smart Education: Reframing Applied Learning to Support Metacognition

This chapter investigates the profound influence of intelligent virtual assistants (IVAs) on the educational domain, specifically in the realm of individualized learning and the instruction of writing abilities and content creation. IVAs, incorporating generative AI technologies such as ChatGPT and Stable Diffusion, hold the potential to bring about a paradigm shift in educational programs, emphasizing the enhancement of advanced metacognitive capacities rather than the fundamentals of communication. The subsequent recommendations stress the need to cultivate enduring proficiencies and ascertain tailored learning approaches for each learner, which will be indispensable for success in the evolving job market. In this context, prompt engineering is emerging as a vital competency, while continuous reskilling and lifelong learning become professional requisites. The proposed innovative method for teaching writing skills and content generation advocates for a reconfiguration of curricula to concentrate on applied learning techniques that accentuate the value of contextual judgment as a central pedagogical tenet and the mastery of sophisticated metacognitive abilities, which will be pivotal in the future of work

Computer assisted instruction for students studying basic logic at the 9th grade level

A system of Computer Assisted Instruction for Students Studying Basic Logic at the 9th Grade Level was written in Prolog running on a Digital Equipment Vax 11/780 under the Unix1 operating system. The program is a straight-forward approach to teaching propositional logic. A three layer menu structure is used to inform students of lessons, subtopics, and help available. Example questions are generated after each subtopic, and student scores are displayed for immediate reinforcement or correction. Scores are also saved for storage on the student\u27s progress chart file. A help function enables the student to review explanations of terminolgy, review truth tables, print a student handbook, and either display or print the student\u27s progress chart. In addition, the teacher may open a student account, close a student account, print a classlist, print progress charts, or print the teacher\u27s handbook. The input/output is not up to par due to the limitations imposed by the Prolog C-Interpreter. Pretests and Posttests are only available interactively with the aid of the teacher

Intelligent tutoring systems for systems engineering methodologies

The general goal is to provide the technology required to build systems that can provide intelligent tutoring in IDEF (Integrated Computer Aided Manufacturing Definition Method) modeling. The following subject areas are covered: intelligent tutoring systems for systems analysis methodologies; IDEF tutor architecture and components; developing cognitive skills for IDEF modeling; experimental software; and PC based prototype

Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Models

In the challenging field of introductory programming, high enrollments and failure rates drive us to explore tools and systems to enhance student outcomes, especially automated tools that scale to large cohorts. This paper presents and evaluates the dcc --help tool, an integration of a Large Language Model (LLM) into the Debugging C Compiler (DCC) to generate unique, novice-focused explanations tailored to each error. dcc --help prompts an LLM with contextual information of compile- and run-time error occurrences, including the source code, error location and standard compiler error message. The LLM is instructed to generate novice-focused, actionable error explanations and guidance, designed to help students understand and resolve problems without providing solutions. dcc --help was deployed to our CS1 and CS2 courses, with 2,565 students using the tool over 64,000 times in ten weeks. We analysed a subset of these error/explanation pairs to evaluate their properties, including conceptual correctness, relevancy, and overall quality. We found that the LLM-generated explanations were conceptually accurate in 90% of compile-time and 75% of run-time cases, but often disregarded the instruction not to provide solutions in code. Our findings, observations and reflections following deployment indicate that dcc-help provides novel opportunities for scaffolding students' introduction to programming.Comment: 7 pages, 2 figures. Accepted in SIGCSE'2

A Generic Intelligent Architecture for Computer-Aided Training of Procedural Knowledge

Intelligent Tutoring System (ITS) development is a knowledge-intensive task, suffering from the same knowledge acquisition bottleneck that plagues most Artificial Intelligence (AI) systems. This research presents an architecture that requires knowledge only in the form of a shallow knowledge base and a simulation to produce a training system. The knowledge base provides the basic procedural knowledge while the simulation provides context. The remainder of the knowledge required for training is learned through the interaction of these components in a state-space scenario exploration process and inductive machine learning. These knowledge components are used only at the interface level, allowing the internal representation to take any form that meets the interface requirements. A prototype of this architecture is implemented as a proof-of-concept to illustrate the viability of the key knowledge acquisition techniques

COMPUTER-ASSISTED INTERVENTION (CAI) TO ENHANCE MATHEMATICAL LEARNING OF AUTISTIC STUDENTS: A CASE STUDY IN MELAKA

Purpose of the study: To evaluate the efficacy of a CAI called MathTutor in helping autistic students to learn addition in mathematics. Methodology: A pre-post-test experimental model was employed in the study. The study participants included three autistic male students who possessed all the prerequisite skills. Main findings and novelty: The study participants were found to have been benefitted which indicates that MathTutor improves their learning abilities as an effective instructional strategy. The use of CAI enabled the participants to memorise their lessons and actively take part during the entire lesson. Applications of this study: The study is beneficial for people involved in caring for autistic students with the help of computer assisted application. This will ease the burden of the carer takers of autistic children in dealing with their conditions while at the same enabling them to navigate the world

Web-based computer assisted laboratory instruction

The feasibility of computer-assisted instruction in a practical laboratory has been explored in this work. Computer assisted instruction (CAI), in which educational instruction is delivered through a computer, has been a popular area of research and development. Computer assisted laboratory instruction (CALI), on the other hand, has not been systematically studied in the past as literature reveals. In the work conducted in this research, the concept of CALI has been examined by developing a web-based multi-media CALI package for Control Systems laboratory that is used by around 100 students annually in the School of Electrical, Computer and Telecommunications Engineering, University of Wollongong. Some elements of Intelligent Tutoring Systems (ITS) have been also incorporated to increase the flexibility of the instruction provided. A systematic approach has been employed to develop the specifications of the package and design its structure to ensure its effectiveness. The latest tools in Web development have been employed to achieve all the defined specifications efficiently and systematically. The outcome is a system that has proved very effective in its operation and instruction for the students in the laboratory. In addition to the specific results and benefits produced directly as the result of employing the package in Control Laboratory, the study has also generated outcomes that are generic and can be considered in the application of the approach in any practical laboratory