4-5 November 2021, Mediterranean Palace Hotel, Thessalonki, Greece
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B05: Mobile learning models, theory and pedagogy
Integration of Software and Hardware AI Learning Models in the SEPT Learning Factory
1McMaster University, Canada; 2Idle Hands Development; 3Roboteurs Inc.
The learning factory developed in the School of Engineering Practice and Technology at McMaster University is an academic entity that focuses on education, applied research and training. One of the current focuses of this learning factory is the integration of Artificial Intelligence (AI) learning models that support the delivery of AI-related curricula. The development and use of applications related to prediction systems are presented, and an approach to develop an AI prediction model for machine health monitoring used for education and training is described. Four small cyber-physical systems that have been designed, build, and put in operation for demonstration and applied research on AI technology topics are de-scribed. AI based vision systems for quality monitoring, object detection, gesture recognition, and facial recognition are also described. The readers can contact the authors to get detailed information about the hardware, software modules, libraries and procedures used to teach the neural networks and develop the prediction models.
The Learners’ Perceptions Of Learning Design For Mobile MOOCs
1Norwegian University of Science and Technology, Norway; 2University of Nicosia, Cyprus
More and more users participate in MOOCs via mobile devices. Mobile MOOC is emerging as a new learning paradigm for many education providers around the world and is becoming more prominent during the COVID-19 pandemic. Very little is known to date on learning design for mobile MOOCs. To address that knowledge gap, this paper is focusing on learning design for mobile MOOC and discusses the opinions of experienced MOOC users on crucial aspects of learning design of a mobile MOOC, focusing on whether these users would prefer a mobile MOOC over a conventional MOOC on a number of crucial learning design aspects. Also, we asked on their preference between mobile MOOC and a conventional MOOC, in general.Sixty participants answered the survey. In general, a mobile MOOC format would be the preferred format to half of them. Participants think that MOOCs’ accessibility and flexibility are better supported using a mobile MOOC format. Yet, they think that the ability to concentrate, motivation, self-study/self-learning, and self-regulated learning are better supported in a conventional MOOC format. It seems that the creation of an inclusive mobile learning environment that would also support deep learning using MOOCs is a difficult problem.
Learning Analytics - Survey and Practical Considerations for Intelligent Education
1Technical University of Sofia, Bulgaria; 2Institute of Information and Communication Technologies, Bulgarian Academy of Sciences
Todays, learning analytics is a vital component in the concept of intelligent education. Analytical tools give many benefits for educators and enable a smart educational process. Our research hypothesis is that learning analytics will provide essential information for achieving adaptable learning paths, better learning performance, and as a result – a more efficient educational process. The paper aims to present a survey of the used learning analytics tools in contemporary education and their applicability for personalization and learning in groups. It also shows how clustering methods can be applied for grouping students with similar learning performance or interests to support learning in groups. Another issue that is considered is how learning analytics can work together with knowledge-based intelligent approaches to achieve personalized and adaptive tutoring.
An Extended Technology Acceptance Model in the Context of Mobile Learning for Primary School Students
Universidad Politécnica de Madrid, Spain
In recent years, the use of mobile learning (M-learning) has significantly increased in educational and academic settings with the growth of mobile technology (MT) and many studies have been realized. But the literature reveals that most of the previous research focused on the field of MT acceptance in high school and university. Also, lack of sufficient research is clearly seen on the acceptance of MT and the use of the M-learning in primary school due to the corona pandemic and its negative effects e.g., the absence of learners in educational settings. Hence, this lack has motivated us to present an extended technology acceptance model (TAM) in the context of M-learning for primary school students. This quantitative non-experimental research has investigated the possibility of using TAM for identifying effective factors on M-learning acceptance by primary school students as a user acceptance model for academic intentions and determined these factors through testing TAM in the school setting. Experimental findings prove that perceived ease of use, perceived enjoyment, perceived convenience, and perceived usefulness had an effect on attitude toward using MT by primary school students. Also, continuance intention to use was directly influenced by perceived usefulness and attitude toward using constructs.
Exploring The Utilization Of Online Open-Source Environments For Mobile Application Development In The Vocational Education And Training Curriculum
UNIVERSITY OF PIRAEUS, Greece
This paper focuses on the pedagogical use of an open-source online environment for Vocational Education and Training (VET), namely the Thunkable. The use of the online environments in VET is a teaching area that has been growing exponentially in recent years, especially for the postgraduate curriculums (4th post-secondary year and Vocational Training Institutes). Thunkable is an open-source online environment that can be used for students and non-programmers, both for their introduction to programming and for the development of mobile applications. This environment is web-based and thus provides the possibility of teaching it in distance education. Its comparative advantage over other mobile application development training environments is that it does not require a tablet or smartphone to preview the application. In this work, we explore the possibilities provided by the online mobile application development environment Thunkable, which provides a visual environment for mobile application development through blocks, as well as its pedagogical utilization in the teaching of similar courses. We also expect such VET teaching scenarios to have the most favorable youth labor market outcome. Besides all, through that scenario, we want to improved collaboration, individual empowerment, and the development of apprentices in VET.
HAS-200 Lab Virtualization in Times of Pandemic
UNIVERSIDAD DISTRITAL - FRANCISCO JOSE DE CALDAS, Colombia
Virtual environments reflect the importance of integrating tools that facilitate teaching within educational classrooms. These virtual environments integrate models of a practical and didactic type, which, under simulations of real environments and situations, generate design methods in the student throughout the entire life cycle of a product, from their creation from a need, their design, efficient and autonomous manufacturing methods.
The main purpose of this work is to contribute, to the academic community, a virtual model of the Flexible Manufacturing System (FMS) HAS-200 that is in the Universidad Distrital – Francisco José de Caldas. The generated model has very precise dimensions and also a basic operation simulation, with the possibility of carrying out routine simulations to design process improvements.
The methodology used in this work was developed in three phases: conceptualization, modelling and simulation. By integrating reliable information sources, each of the stations was conceptualized. With a computer-aided design (CAD) tool, each station was modelled, with its respective assembled elements. A general assemblage of the FMS was implemented to analyze movements and simulations. Motion simulation based on real operating data was developed in the NX-Siemens® software.
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