Artificial Intelligence in Education

Artificial Intelligence (AI) technologies have been researched in educational contexts for more than 30 years (Woolf 1988; Cumming and McDougall 2000; du Boulay 2016). More recently, commercial AI products have also entered the classroom. However, while many assume that Artificial Intelligence in Education (AIED) means students taught by robot teachers, the reality is more prosaic yet still has the potential to be transformative (Holmes et al. 2019). This chapter introduces AIED, an approach that has so far received little mainstream attention, both as a set of technologies and as a field of inquiry. It discusses AIED’s AI foundations, its use of models, its possible future, and the human context. It begins with some brief examples of AIED technologies

Data mining tool for academic data exploitation: literature review and first architecture proposal

Using data for making decisions is not new; companies use complex computations on customer data for business intelligence or analytics. Business intelligence techniques can discern historical patterns and trends from data and can create models that predict future trends and patterns. Analytics, broadly defined, comprises applied techniques from computer science, mathematics, and statistics for extracting usable information from very large datasets. Data itself is not new. Data has always been generated and used to inform decision-making. However, most of this was structured and organised, through regular data collections, surveys, etc. What is new, with the invention and dominance of the Internet and the expansion of digital systems across all sectors, is the amount of unstructured data we are generating. This is what we call the digital footprint: the traces that individuals leave behind as they interact with their increasingly digital world. Data analytics is the process where data is collected and analysed in order to identify patterns, make predictions, and inform business decisions. Our capacity to perform increasingly sophisticated analytics is changing the way we make predictions and decisions, with huge potential to improve competitive intelligence. These examples suggest that the actions from data mining and analytics are always automatic, but that is less often the case. Educational Data Mining (EDM) and Learning Analytics (LA) have the potential to make visible data that have heretofore gone unseen, unnoticed, and therefore unactionable. To help further the fields and gain value from their practical applications, the recommendations are that educators and administrators: • Develop a culture of using data for making instructional decisions; • Involve IT departments in planning for data collection and use; • Be smart data consumers who ask critical questions about commercial offerings and create demand for the most useful features and uses; • Start with focused areas where data will help, show success, and then expand to new areas; • Communicate with students and parents about where data come from and how the data are used; • Help align state policies with technical requirements for online learning systems. This report documents the first steps conducted within the SPEET1 ERASMUS+ project. It describes the conceptualization of a practical tool for the application of EDM/LA techniques to currently available academic data. The document is also intended to contextualise the use of Big Data within the academic sector, with special emphasis on the role that student profiles and student clustering do have in support tutoring actions. The report describes the promise of educational data mining (seeking patterns in data across many student actions), learning analytics (applying predictive models that provide actionable information), and visual data analytics (interactive displays of analyzed data) and how they might serve the future of personalized learning and the development and continuous improvement of adaptive systems. How might they operate in an adaptive learning system? What inputs and outputs are to be expected? In the next sections, these questions are addressed by giving a system-level view of how data mining and analytics could improve teaching and learning by creating feedback loops. Finally, the proposal of the key elements that conform a software application that is intended to give support to this academic data analysis is presented. Three different key elements are presented: data, algorithms and application architecture. From one side we should have a minimum data available. The corresponding relational data base structure is presented. This basic data can always be complemented with other available data that may help to decide or/and to explain decisions. Classification algorithms are reviewed and is presented how they can be used for the generation of the student clustering problem. A convenient software architecture will act as an umbrella that connects the previous two parts. The document is intended to be useful for a first understanding of academic data analysis. What we can get and what we do need to do. This is the first of a series of reports that taken all together will provide a complete and consistent view towards the inclusion of data mining as a helping hand in the tutoring action.European UnionProgramme: Erasmus+ Project Reference: 2016-1-ES01-KA203-025452info:eu-repo/semantics/draf

Framing automatic grading techniques for open-ended questionnaires responses. A short survey

The assessment of students' performances is one of the essential components of teaching activities, and it poses different challenges to teachers and instructors, especially when considering the grading of responses to open-ended questions (i.e., short-answers or essays). Open-ended tasks allow a more in-depth assessment of students' learning levels, but their evaluation and grading are time-consuming and prone to subjective bias. For these reasons, automatic grading techniques have been studied for a long time, focusing mainly on short-answers rather than long essays. Given the growing popularity of Massive Online Open Courses and the shifting from physical to virtual classrooms environments due to the Covid-19 pandemic, the adoption of questionnaires for evaluating learning performances has rapidly increased. Hence, it is of particular interest to analyze the recent effort of researchers in the development of techniques designed to grade students' responses to open-ended questions. In our work, we consider a systematic literature review focusing on automatic grading of open-ended written assignments. The study encompasses 488 articles published from 1984 to 2021 and aims at understanding the research trends and the techniques to tackle essay automatic grading. Lastly, inferences and recommendations are given for future works in the Learning Analytics field

Framing automatic grading techniques for open-ended questionnaires responses. A short survey

The assessment of students' performances is one of the essential components of teaching activities, and it poses different challenges to teachers and instructors, especially when considering the grading of responses to open-ended questions (i.e., short-answers or essays). Open-ended tasks allow a more in-depth assessment of students' learning levels, but their evaluation and grading are time-consuming and prone to subjective bias. For these reasons, automatic grading techniques have been studied for a long time, focusing mainly on short-answers rather than long essays. Given the growing popularity of Massive Online Open Courses and the shifting from physical to virtual classrooms environments due to the Covid-19 pandemic, the adoption of questionnaires for evaluating learning performances has rapidly increased. Hence, it is of particular interest to analyze the recent effort of researchers in the development of techniques designed to grade students' responses to open-ended questions. In our work, we consider a systematic literature review focusing on automatic grading of open-ended written assignments. The study encompasses 488 articles published from 1984 to 2021 and aims at understanding the research trends and the techniques to tackle essay automatic grading. Lastly, inferences and recommendations are given for future works in the Learning Analytics field

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</jats:p

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

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

Unleashing Process Mining for Education: Designing an IT-Tool for Students to Self-Monitor their Personal Learning Paths

The ability of students to self-monitor their learning paths is in demand as never before due to the recent rise of online education formats, which entails less interaction with lecturers. Recent advantages in educational process mining (EPM) offer new opportunities to monitor students’ learning paths by processing log data captured by technology-mediated learning environments. However, current literature falls short on providing user-centered design principles for ITtools which can monitor learning paths using EPM. Hence, in this paper, we examine how to design a self-monitoring tool that supports students to evaluate their learning paths. Based on theoretical insights of 66 papers and nine user interviews, we propose seven design principles for an IT-tool which facilitates self-monitoring for students based on EPM. Further, we evaluate the design principles with seven potential users. Our results demonstrate a promising approach to help students improve their self-efficacy in their individual learning process using EPM

Artificial Intelligence-Based Tools in Research Writing : Current Trends and Future Potentials

Questions begin to arise around to what extent research writing and training can be supported electronically. The emergence of Artificial Intelligence (AI) technologies have triggered a tremendous interest among educational technologists. One area that has received much attention is AI-based tools, developed to assist researchers in the writing process. AI-powered writing tools aim to not only ease the process of research writing but also to enhance the quality of critical analysis particularly in the aspect of literature review and language style. Despite the trends in adopting AI components within research writing, there are still-limited efforts to examine its implementation, strengths and weaknesses. This chapter reviews some of the key studies concerning AI in research writing as well as an extensive review of existing tools by covering their AI-based features, affordances and constraints. The chapter also includes a discussion on the future potentials of AI implementation with regard to research writing. This chapter would serve as a good reference to uncover the hidden potentials of AI-based tools in assisting students to produce high-quality research writing