Mobile VLE vs. Mobile PLE: How Informal is Mobile Learning?

Mobile Learning Systems are often described as supporting informal learning; as such they are a good fit to the idea of Personal Learning Environments (PLEs), software systems that users choose and tailor to fit their own learning preferences. This paper explores the question of whether existing m-learning research is more in the spirit of PLEs or Virtual Learning Environments (VLEs). To do this we survey the mobile learning systems presented at M-Learn 2007 in order to see if they might be regarded as informal or formal learning. In order to categorise the systems we present a four dimensional framework of formality, based on Learning Objective, Learning Environment, Learning Activity and Learning Tools. We use the framework to show that mobile systems tend to be informal in terms of their environment, but ignore the other factors. Thus we can conclude that despite the claims of m-learning systems to better support informal and personal learning, today’s m-learning research is actually more in the spirit of a VLE than a PLE, and that there remains a great deal of unexplored ground in the area of Mobile PLE systems

Enhancing learning through self-explanation

Self-explanation is an effective teaching/learning strategy that has been used in several intelligent tutoring systems in the domains of Mathematics and Physics to facilitate deep learning. Since all these domains are well structured, the instructional material to self-explain can be clearly defined. We are interested in investigating whether self-explanation can be used in an open-ended domain. For this purpose, we enhanced KERMIT, an intelligent tutoring system that teaches conceptual database design. The resulting system, KERMIT-SE, supports self-explanation by engaging students in tutorial dialogues when their solutions are erroneous. We plan to conduct an evaluation in July 2002, to test the hypothesis that students will learn better with KERMIT-SE than without self-explanation

Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System

In the context of building an intelligent tutoring system (ITS), which improves student learning outcomes by intervention, we set out to improve prediction of student problem outcome. In essence, we want to predict the outcome of a student answering a problem in an ITS from a video feed by analyzing their face and gestures. For this, we present a novel transfer learning facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We model the temporal structure of video sequences of students solving math problems using a recurrent neural network architecture. Additionally, we extend the largest dataset of student interactions with an intelligent online math tutor by a factor of two. Our final model, coined ATL-BP (Affect Transfer Learning for Behavior Prediction) achieves an increase in mean F-score over state-of-the-art of 45% on this new dataset in the general case and 50% in a more challenging leave-users-out experimental setting when we use a user-personalized training scheme

Classification of E-Learning Tools

У статті висвітлені проблеми класифікації та розробки вимог у галузі програмних засобів навчального призначення. Запропоновано класифікацію засобів згідно до типів діяльності, для підтримки яких вони можуть бути застосовані. Охарактеризовано головні типи засобів навчання з елементами штучного інтелекту.In this paper the problems of classification and estimation of computer-assisted learning tools are described. Taxonomy of computer tools according to learner activity types is proposed. Main types of artificial intelligence learning tools are characterized

Machine Learning Methods for Spoken Dialogue Simulation and Optimization

Computers and electronic devices are becoming more and more present in our day-to-day life. This can of course be partly explained by their ability to ease the achievement of complex and boring tasks, the important decrease of prices or the new entertainment styles they offer. Yet, this real incursion in everybody's life would not have been possible without an important improvement of Human-Computer Interfaces (HCI). This is why HCI are now widely studied and become a major trend of research among the scientific community. Designing “user-friendly” interfaces usually requires multidisciplinary skills in fields such as computer science, ergonomics, psychology, signal processing etc. In this chapter, we argue that machine learning methods can help in designing efficient speech-based humancomputer interfaces

Systems and Methods of Knowledge Simulation in a Unite Information-Educational Space

статті виявлено та систематизовано головні напрямки розробки та впровадження засобів, що ґрунтуються на знаннях у єдиному інформаційно-освітньому просторі, виявлено перспективні шляхи їх впровадження та застосування.In this article the main directions of development and implementation of knowledge-based tools in a single information-educational space are identified and systematized. The promising ways of their implementation and enforcement are revealed

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

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