Data-driven fuzzy rule generation and its application for student academic performance evaluation

Several approaches using fuzzy techniques have been proposed to provide a practical method for evaluating student academic performance. However, these approaches are largely based on expert opinions and are difficult to explore and utilize valuable information embedded in collected data. This paper proposes a new method for evaluating student academic performance based on data-driven fuzzy rule induction. A suitable fuzzy inference mechanism and associated Rule Induction Algorithm is given. The new method has been applied to perform Criterion-Referenced Evaluation (CRE) and comparisons are made with typical existing methods, revealing significant advantages of the present work. The new method has also been applied to perform Norm-Referenced Evaluation (NRE), demonstrating its potential as an extended method of evaluation that can produce new and informative scores based on information gathered from data

Learning Path Construction in e-Learning – What to Learn and How to Learn?

Whether in traditional or e learning, it is important to consider: what to learn, how to learn, and how well students have learned. Since there are various types of students with different learning preferences, learning styles, and learning abilities, it is not easy to provide the best learning approach for a specific student. Designing learning contents for different students is very time consuming and tedious for teachers. No matter how the learning process is carried out, both teachers and students must be satisfied with students’ learning performance. Therefore, it is important to provide helpful teaching and learning guidance for teachers and students. In order to achieve this, we proposed a fined-grained outcome-based learning path model, which allows teachers to explicitly formulate learning activities as the learning units of a learning path. This allows teachers to formulate the assessment criteria related to the subject-specific knowledge and skills as well as generic skills, so that the pedagogy could be defined and properly incorporated. Apart from defining the pedagogical approaches, we also need to provide tailored learning contents of the courses, so that different types of students can better learn the knowledge according to their own learning abilities, knowledge backgrounds, etc. On the other hand, those learning contents should be well-structured, so that students can understand them. To achieve this, we have proposed a learning path generation method based on Association Link Network to automatically identify the relationships among different Web resources. This method makes use of the Web resources that can be freely obtained from the Web to form well-structured learning resources with proper sequences for delivery. Although the learning path defines what to learn and how to learn, we still needed to monitor student learning progress in order to determine proper learning contents and learning activities in an e-Learning system. To address the problem, we proposed the use of student progress indicators based on Fuzzy Cognitive Map to analyze both performance and non-performance attributes and their causal relationships. The aim is to help teachers improve their teaching approaches and help students reflect their strengths and weaknesses in learning. . This research focuses on the intelligent tutoring e-Learning system, which provides an intelligent approach to design and delivery learning activities in a learning path. Many experiments and comparative studies on both teachers and students have been carried out in order to evaluate the research of this PhD thesis. The results show that our research can effectively help teachers generate high quality learning paths, help students improve their learning performance, and offer both teachers and students a better understanding on student learning progress

The Interface of Technology in Culinary Arts Education

Introduction: A culinary educator must make many decisions that affect the day-to-day activities in both the classroom and the lab. One of the more important decisions is how to select the most appropriate technology to implement for use in teaching and administrative activities. The research presented here is intended to help the educator identify specific needs, decide where the use of technology is desirable, and offer information designed to help the educator make an informed decision about using technology as a teaching tool. Purpose Statement: The purpose of this paper is to inform the culinary educator about the technology available for use in both the classroom and the lab setting. There is an ever-increasing pool of technology, making it more important than ever that the educator choose the appropriate lab/kitchen equipment and software programs for use in a specific culinary program. Making an informed decision ensures maximum usefulness of the technology in the setting

An approach to automatic learning assessment based on the computational theory of perceptions

E-learning systems output a huge quantity of data on a learning process. However, it takes a lot of specialist human resources to manually process these data and generate an assessment report. Additionally, for formative assessment, the report should state the attainment level of the learning goals defined by the instructor. This paper describes the use of the granular linguistic model of a phenomenon (GLMP) to model the assessment of the learning process and implement the automated generation of an assessment report. GLMP is based on fuzzy logic and the computational theory of perceptions. This technique is useful for implementing complex assessment criteria using inference systems based on linguistic rules. Apart from the grade, the model also generates a detailed natural language progress report on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. This is illustrated by applying the model to the assessment of Dijkstra’s algorithm learning using a visual simulation-based graph algorithm learning environment, called GRAPH

Challenges for IT-Enabled Formative Assessment of Complex 21st Century Skills

In this article, we identify and examine opportunities for formative assessment provided by information technologies (IT) and the challenges which these opportunities present. We address some of these challenges by examining key aspects of assessment processes that can be facilitated by IT: datafication of learning; feedback and scaffolding; peer assessment and peer feedback. We then consider how these processes may be applied in relation to the assessment of horizontal, general complex 21st century skills (21st CS), which are still proving challenging to incorporate into curricula as well as to assess. 21st CS such as creativity, complex problem solving, communication, collaboration and self-regulated learning contain complex constructs incorporating motivational and affective components. Our analysis has enabled us to make recommendations for policy, practice and further research. While there is currently much interest in and some progress towards the development of learning/assessment analytics for assessing 21st CS, the complexity of assessing such skills, together with the need to include affective aspects means that using IT-enabled techniques will need to be combined with more traditional methods of teacher assessment as well as peer assessment for some time to come. Therefore learners, teachers and school leaders must learn how to manage the greater variety of sorts and sources of feedback including resolving tensions of inconsistent feedback from different sources

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl