A Survey of Optimized Learning Pathway Planning and Assessment Paper Generation with Swarm Intelligence

2012 ◽  
pp. 1933-1950
Author(s):  
Lung-Hsiang Wong ◽  
Chee-Kit Looi
Keyword(s):  
Artificial Intelligence ◽  
Swarm Intelligence ◽  
Learning Objects ◽  
Critical Discussion ◽  
Learning Systems ◽  
Optimal Learning ◽  
Research Issues ◽  
Adaptive Selection ◽  
Major Direction ◽  
Learning Pathways

One major direction in research on technology-enabled learning systems revolves round the notion of generating optimal learning pathways. Two examples of the application areas that could be presented as search and optimization problem in the context of Artificial Intelligence are:- (1) Adaptive selection and sequencing of learning objects based on the learning profiles, preferences and abilities of individual learners; (2) Automatic composition of assessment or examination papers based on instructors‘ specifications. In this chapter, we present a critical discussion of the research which is concerned with the application of the paradigm of “swarm intelligence” in these two areas. The main aim of this survey is to highlight the new trends and key research achievements that have been realised in the last few years. We will also outline a range of relevant research issues and challenges that have been generated by this body of work.

A Survey of Optimized Learning Pathway Planning and Assessment Paper Generation with Swarm Intelligence

2011 ◽  
pp. 285-302 ◽  
Author(s):  
Lung-Hsiang Wong ◽  
Chee-Kit Looi
Keyword(s):  
Artificial Intelligence ◽  
Swarm Intelligence ◽  
Learning Objects ◽  
Critical Discussion ◽  
Learning Systems ◽  
Optimal Learning ◽  
Research Issues ◽  
Adaptive Selection ◽  
Major Direction ◽  
Learning Pathways

One major direction in research on technology-enabled learning systems revolves round the notion of generating optimal learning pathways. Two examples of the application areas that could be presented as search and optimization problem in the context of Artificial Intelligence are:- (1) Adaptive selection and sequencing of learning objects based on the learning profiles, preferences and abilities of individual learners; (2) Automatic composition of assessment or examination papers based on instructors‘ specifications. In this chapter, we present a critical discussion of the research which is concerned with the application of the paradigm of “swarm intelligence” in these two areas. The main aim of this survey is to highlight the new trends and key research achievements that have been realised in the last few years. We will also outline a range of relevant research issues and challenges that have been generated by this body of work.

scholarly journals The swarm intelligence algorithms and their application for the educational data analysis

2019 ◽  
Vol 23 (5) ◽  
pp. 33-43
Author(s):  
Y. Yu. Dyulicheva
Keyword(s):  
Data Analysis ◽  
Swarm Intelligence ◽  
Learning Objects ◽  
Ant Algorithm ◽  
First Year ◽  
Optimal Learning ◽  
First Year Students ◽  
Learning Content ◽  
Learning Path ◽  
Level Of Knowledge

The purpose of the paper is the investigation of the modern approaches and prospects for the application of swarm intelligence algorithms for educational data analysis, as well as the possibility of using of ant algorithm modifications for organizing educational content in adaptive systems for conducting project seminars.Materials and methods. The review of the modern articles on the educational data analysis based on swarm intelligence algorithms is provided; the approaches to solving problem of the optimal learning path construction (optimal organization of the learning objects) based on the algorithm and its modifications taking into account the students’ performance in the process of the optimal learning path construction are investigated; the application of particle swarm optimization and its modification based on Roccio algorithm for the reduction of curse dimension in the problem of the auto classifying questions; the application of ant algorithm, bee colony algorithm and bat algorithm for recommender system construction are studied; the prediction of students’ performance based on particle swarm optimization is researched in the article. The modification of ant algorithm for optimal organization of learning objects at projects seminars is proposed.Results. The modern approaches based on swarm intelligence algorithms to problem solving in educational data analysis are investigated. The various approaches to pheromones updating (their evaporation) when building the optimal learning path based on students’ performance data and search of group with “similar" students are studied; the abilities of the hybrid swarm intelligence algorithms for recommendation construction are investigated.Based on the modification of ant algorithm, the approach to the learning content organization at project seminars with individual preferences and students’ level of basic knowledge is proposed. The python classes are developed: the class for statistical data processing; the classfor modifica -tion of ant algorithm, taking into account the current level of knowledge and interest of student in studying a specific topic at the project seminar; the class for optimal sequence of the project seminars ’ topics for students. The developed classes allow creating the adaptive system that helps first year students with a choice of topics of project seminars.Conclusion. According to the results of the study, we can conclude about the effectiveness of swarm intelligence algorithms usage to solve a wide range of tasks connected with learning content and students’ data analysis in the e-learning systems and perspectives to hybrid approaches development based on swarm intelligence algorithms for realizing the adaptive learning systems on the paradigm of “demand learning".The results can be used to automate the organization of learning content during project seminars for the first-year students, when it is important to understand the basic level of knowledge and students’ interest in learning new technologies.

Robot Teaching, Pedagogy, and Policy

2020 ◽  
pp. 787-803
Author(s):  
Elana Zeide
Keyword(s):  
Artificial Intelligence ◽  
Analytical Methods ◽  
Personalized Learning ◽  
Educational Process ◽  
Learning Systems ◽  
Policy Decisions ◽  
Public Discussion ◽  
Teaching Pedagogy ◽  
Do So

This chapter looks at the use of artificial intelligence (AI) in education, which immediately conjures the fantasy of robot teachers, as well as fears that robot teachers will replace their human counterparts. However, AI tools impact much more than instructional choices. Personalized learning systems take on a whole host of other educational roles as well, fundamentally reconfiguring education in the process. They not only perform the functions of robot teachers but also make pedagogical and policy decisions typically left to teachers and policymakers. Their design, affordances, analytical methods, and visualization dashboards construct a technological, computational, and statistical infrastructure that literally codifies what students learn, how they are assessed, and what standards they must meet. However, school procurement and implementation of these systems are rarely part of public discussion. If they are to remain relevant to the educational process itself, as opposed to just its packaging and context, schools and their stakeholders must be more proactive in demanding information from technology providers and setting internal protocols to ensure effective and consistent implementation. Those who choose to outsource instructional functions should do so with sufficient transparency mechanisms in place to ensure professional oversight guided by well-informed debate.

scholarly journals Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 18
Author(s):  
Pantelis Linardatos ◽  
Vasilis Papastefanopoulos ◽  
Sotiris Kotsiantis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Learning Systems ◽  
Model Complexity ◽  
Learning Models ◽  
New Methods ◽  
Industrial Adoption ◽  
Machine Learning Models ◽  
The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

scholarly journals HyCo – An Authoring Tool to Create Semantic Learning Objects for Web-Based E-learning Systems

2004 ◽  
pp. 344-348 ◽  
Author(s):  
Francisco J. García ◽  
Adriana J. Berlanga ◽  
Maria N. Moreno ◽  
Javier García ◽  
Jorge Carabias
Keyword(s):  
Learning Objects ◽  
Learning Systems ◽  
Authoring Tool ◽  
Web Based ◽  
Semantic Learning ◽  
E Learning

scholarly journals Artificial Intelligence in Thyroid Field. A Comprehensive Review

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4740
Author(s):  
Fabiano Bini ◽  
Andrada Pica ◽  
Laura Azzimonti ◽  
Alessandro Giusti ◽  
Lorenzo Ruinelli ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cognitive Abilities ◽  
Medical Diagnostics ◽  
Critical Discussion ◽  
Research Field ◽  
Response To Treatment ◽  
Key Concepts ◽  
Mathematical Algorithms

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

Ubiquitous Multi-Agent Context-Aware System for Enhancing Teaching-Learning Processes Adapted to Student Profile

2014 ◽  
pp. 237-253
Author(s):  
Demetrio Ovalle ◽  
Oscar Salazar ◽  
Néstor Duque
Keyword(s):  
Learning Objects ◽  
Learning Systems ◽  
Mobile Technologies ◽  
Context Aware ◽  
Student Centered ◽  
Student Profile ◽  
Multi Agent ◽  
Use Of The Internet ◽  
Context Aware System ◽  
Teaching Learning

The need for ubiquitous systems that allow access to computer systems from anywhere at anytime and the massive use of the Internet has prompted the creation of e-learning systems that can be accessed from mobile smart phones, PDA, or tablets, taking advantage of the current growth of mobile technologies. The aim of this chapter is to present the advantages brought by the integration of ubiquitous computing-oriented along with distributed artificial intelligence techniques in order to build student-centered context-aware learning systems. Based on this model, the authors propose a multi-agent context-aware u-learning system that offers several functionalities such as context-aware learning planning, personalized course evaluation, selection of learning objects according to student’s profile, search of learning objects in repository federations, search of thematic learning assistants, and access of current context-aware collaborative learning activities involved. Finally, the authors present some solutions considering the functionalities that a u-learning multi-agent context-aware system should exhibit.

A Behavioral Economics Approach to Digitalization

2019 ◽  
pp. 107-122
Author(s):  
Dirk Beerbaum ◽  
Julia Margarete Puaschunder
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Media ◽  
Behavioral Economics ◽  
Field Research ◽  
Learning Systems ◽  
Business Reporting ◽  
Intelligent Machines ◽  
Private Investors ◽  
Coding Rules

Technological improvement in the age of information has increased the possibilities to control the innocent social media users or penalize private investors and reap the benefits of their existence in hidden persuasion and discrimination. This chapter takes as a case the transparency technology XBRL (eXtensible Business Reporting Language), which should make data more accessible as well as usable for private investors. Considering theoretical literature and field research, a representation issue for principles-based accounting taxonomies exists, which intelligent machines applying artificial intelligence (AI) nudge to facilitate decision usefulness. This chapter conceptualizes ethical questions arising from the taxonomy engineering based on machine learning systems and advocates for a democratization of information, education, and transparency about nudges and coding rules.

Technology Management Through Artificial Intelligence in Open and Distance Learning

2019 ◽  
pp. 338-368 ◽  
Author(s):  
Serap Uğur ◽  
Gulsun Kurubacak
Keyword(s):  
Artificial Intelligence ◽  
Qualitative Research ◽  
Distance Learning ◽  
Technology Management ◽  
Qualitative Research Methods ◽  
Learning Systems ◽  
Distance Learners ◽  
Interpretive Phenomenology ◽  
Open And Distance Learning ◽  
Learning Institutions

Technology management is a management discipline that evaluates the potential of the cutting-edge technology integration to maintain the competitive institutions, and seeks ways to use these potentials for the benefit of the organizations. The technologies that use in open and distance learning institutions for learner enrollment and course follow-ups, software that teachers use both in content presentations and evaluation stages, etc. They need to use technology in many different services and processes in the managerial dimension. In this chapter, which is conducted by using interpretive phenomenology method from qualitative research methods, it was questioned how to integrate artificial intelligence in open and distance learning systems determined within the scope of technology management for a technology-driven international university. Suggestions were made for artificial intelligence applications in the management of open and distance learners.

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