A Literature Review on Intelligent Services Applied to Distance Learning

Distance learning has assumed a relevant role in the educational scenario. The use of Virtual Learning Environments contributes to obtaining a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance, and identify probabilities of dropouts from courses.info:eu-repo/semantics/publishedVersio

Developing Student Model for Intelligent Tutoring System

The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching community to understand the learning style of their students and to cater for the needs of their students. One such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful, constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in planning the training path, supplying feedback information to the pedagogical module of the system. Added to it, the student model is the preliminary component, which stores the information to the specific individual learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural network. Further, neural network and psychometric analysis were used for understanding the student characteristic and determining the student’s classification with respect to their ability. Thus, this study focused on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS by applying the neural network and psychometric analysis. The findings of this research showed that even though the linear regression between real test scores and that of the Final exam scores were marginally weak (37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model a good fit for clustering students in groups according to their common characteristics. This finding is in line with that of the findings discussed in the literature review of this study. Further, the outcome of this research is most likely to generate a new dimension for cluster based student modelling approaches for an online learning environment that uses aptitude tests (MCQ’s) for learners using ITS. The use of psychometric analysis and neural network for student classification makes this study unique towards the development of a new student model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS system for an online learning environment. (Abstract by Author

Learn Smarter, Not Harder – Exploring the Development of Learning Analytics Use Cases to Create Tailor-Made Online Learning Experiences

Our world is significantly shaped by digitalization, fostering new opportunities for technology-mediated learning. Therefore, massive amounts of knowledge become available online. However, concurrently these formats entail less interaction and guidance from lecturers. Thus, learners need to be supported by intelligent learning tools that provide suitable knowledge in a tailored way. In this context, the use of learning analytics in its multifaceted forms is essential. Existing literature shows a proliferation of learning analytics use cases without a systematic structure. Based on a structured literature review of 42 papers we organized existing literature contributions systematically and derived four use cases: learning dashboards, individualized content, tutoring systems, and adaptable learning process based on personality. Our use cases will serve as a basis for a targeted scientific discourse and are valuable orientation for the development of future learning analytics use cases to give rise to the new form of Learning Experience Platforms

An initial exploration of semi-automated tutoring: How AI could be used as support for online human tutors

In this paper, we begin our process of incorporating an AI bot in an online chat tutoring setting as a support for the tutor. We explore how an AI bot could give suggestions for tutor messages, although the human tutor will control how to communicate with the student. Tutoring, an important dimension of networked learning, has long been seen as a beneficial approach to students’ learning. An AI bot has the potential to aid tutors in the tutoring process and contribute to the scalability. The present pilot study was conducted in the tutoring setting of the Math Coach program. In the program, teacher students aid students from upper primary school to upper secondary school in mathematics through an online text-based chat system. Llama2 was used as a large language model (LLM), fine-tuned for Swedish comprehension utilizing the Math Coach system's chat logs. Four coaches, teacher students at a technical Swedish university and active in the Math Coach program, were invited to interact with the AI bot and participate in a group discussion. The coaches interacted individually with the AI bot while the chat conversation was displayed on a monitor so all participants could discuss the interaction while it took place. A semi-structured interview approach was taken and the participants were also encouraged to 'think aloud' about their experience. In the discussions, the coaches expressed surprise by the AI's social aspect. They perceived the AI bot as friendly with a positive attitude and were especially surprised by its ability to correctly place appropriate emojis. The coaches agreed that the AI was able to ask both appropriate and helpful questions and share some good guidance for how to proceed in the problem-solving process. However, they felt that the AI bot was not able to offer sufficient mathematical guidance, oftentimes the AI bot was confidently wrong. It also wrote too long messages, which humans would typically separate into several chat messages, and did not wait for a response but instead moved too quickly towards the solution. Moving forward we plan to address the effects of improved prompts on the AI bot and continue finetuning the LLM. We will continue to conduct pilot studies and eventually conduct more large-scale empirical studies

New measurement paradigms

This collection of New Measurement Paradigms papers represents a snapshot of the variety of measurement methods in use at the time of writing across several projects funded by the National Science Foundation (US) through its REESE and DR K–12 programs. All of the projects are developing and testing intelligent learning environments that seek to carefully measure and promote student learning, and the purpose of this collection of papers is to describe and illustrate the use of several measurement methods employed to achieve this. The papers are deliberately short because they are designed to introduce the methods in use and not to be a textbook chapter on each method. The New Measurement Paradigms collection is designed to serve as a reference point for researchers who are working in projects that are creating e-learning environments in which there is a need to make judgments about students’ levels of knowledge and skills, or for those interested in this but who have not yet delved into these methods

EFEITOS INOVADORES DA INTELIGÊNCIA ARTIFICIAL NA FORMAÇÃO DE PROFESSORES

The goal of this investigation was to see the impact of AI on teacher education. To have a better understanding of how it will impact teacher education in problem-solving, how it will aid in planning, and whether it will promote continuous learning in teacher education. The study allows instructors and students from tertiary institutions in The Turkish Republic of Northern Cyprus to express their views. Three primary research topics guided these investigations, and four semi-structured interview questions were developed to obtain thorough responses to these issues. The opinions of the participants were solicited in person. The findings showed that both students and lecturers were aware and comfortable with AI in educational systems, that AI has had significant effects on problem-solving in teacher education, and that AI is vital for facilitating planning in teacher education. Finally, it was emphasized that the application of AI in teacher education allows for continuous learning, which has benefits as well as drawbacks.O objetivo desta investigação era ver o impacto da IA na formação de professores. Ter uma melhor compreensão de como isso afetará a formação de professores na resolução de problemas, como ajudará no planejamento e se promoverá a aprendizagem contínua na formação de professores. O estudo permite que instrutores e estudantes de instituições terciárias da República Turca do Norte de Chipre expressem as suas opiniões. Três tópicos primários de pesquisa orientaram essas investigações, e quatro perguntas de entrevista semiestruturadas foram desenvolvidas para obter respostas completas a essas questões. As opiniões dos participantes foram solicitadas pessoalmente. As conclusões mostraram que tanto os alunos como os docentes estavam conscientes e confortáveis com a IA nos sistemas educativos, que a IA teve efeitos significativos na resolução de problemas na formação de professores e que a IA é vital para facilitar o planeamento na formação de professores. Por fim, enfatizou-se que a aplicação da IA na formação de professores permite a aprendizagem contínua, o que traz vantagens e desvantagens

The Impact of AI on Teaching and Learning in Higher Education Technology

Thanks to AI, students may now study whenever and wherever they like. Personalized feedback on assignments, quizzes, and other assessments can be generated using AI algorithms and utilised as a teaching tool to help students succeed. This study examined the impact of artificial intelligence in higher education teaching and learning. This study focuses on the impact of new technologies on student learning and educational institutions. With the rapid adoption of new technologies in higher education, as well as recent technological advancements, it is possible to forecast the future of higher education in a world where artificial intelligence is ubiquitous. Administration, student support, teaching, and learning can all benefit from the use of these technologies; we identify some challenges that higher education institutions and students may face, and we consider potential research directions

A Causal-Comparative Study on the Efficacy of Intelligent Tutoring Systems on Middle-Grade Math Achievement

This study is a quantitative examination of intelligent tutoring systems in two similar suburban middle schools (grades 6-8) in the Southeastern United States. More specifically, it is a causal-comparative study purposed with examining the efficacy of intelligent tutoring systems as they relate to math achievement for students at two similar middle schools in the Midlands of South Carolina. The independent variable, use of an intelligent tutoring system in math instruction, is defined as the supplementary use of two intelligent tutoring systems, Pearson’s Math Digits and IXL, for math instruction. The dependent variable is math achievement as determined by the Measures of Academic Progress (MAP) SC 6+Math test. The student data examined is archived MAP SC 6+ Math scores from the 2017-2018 school year. A one-way ANCOVA was used to compare the mean achievement gain scores of both groups, students whose math instruction included intelligent tutoring systems and students whose math instruction did not include intelligent tutoring systems, to establish whether or not there was any statistically significant difference between the adjusted population means of the two independent groups. The results showed that the adjusted mean of posttest scores of students who did not receive math instruction that involved an intelligent tutoring system were significantly higher than those who did

Intelligent Tutoring System for Teaching "Introduction to Computer Science" in Al-Azhar University, Gaza

ITS (Intelligent Tutoring System) is a computer software that supplies direct and adaptive training or response to students without, or with little human teacher interfering. The main target of ITS is smoothing the learning-teaching process using the ultimate technology in computer science. The proposed system will be implemented using the “ITSB” Authoring tool. The book "Introduction To Computer Science" is taught in Al-Azhar University in Gaza as a compulsory subject for students who study at humanities faculties. In this thesis, the researcher demonstrates an intelligent tutoring system for teaching the above mentioned subject. The system was assessed by a group of teachers and students and the results were promising