Educational technology | EPFL Graph Search

Educational technology (commonly abbreviated as edutech, or edtech) is the combined use of computer hardware, software, and educational theory and practice to facilitate learning. When referred to with its abbreviation, edtech, it often refers to the industry of companies that create educational technology. In addition to the practical educational experience, educational technology is based on theoretical knowledge from various disciplines such as communication, education, psychology, sociology, artificial intelligence, and computer science. It encompasses several domains including learning theory, computer-based training, online learning, and m-learning where mobile technologies are used. Definition The Association for Educational Communications and Technology (AECT) has defined educational technology as "the study and ethical practice of facilitating learning and improving performance by creating, using and managing appropriate technological processes and resources".

Different parameters - same prediction. An analysis of learning curves

Using data from student use of educational technologies to evaluate and improve cognitive models of learners is now a common approach in EDM. Such naturally occurring data poses modeling challenges when non-random factors drive what data is collected. Prior work began to explore the potential parameter estimate biases that may result from data from tutoring systems that employ a mastery learning mechanism whereby poorer students get assigned tasks that better students do not. We extend that work both by exploring a wider set of modeling techniques and by using a data set with additional observations of longer-term retention that provide a check on whether judged mastery is maintained. The data set at hand contains math learning data from children with and without developmental dyscalculia. We test variations on logistic regression, including the Additive Factors Model and others explicitly designed to adjust for mastery-based data, as well as Bayesian Knowledge Tracing (BKT). We find these models produce similar prediction accuracy (though BKT is worse), but have different parameter estimation patterns. We discuss implications for use and interpretation of these different models.

2014

Perspectives to Technology-Enhanced Learning and Teaching in Mathematical Learning Difficulties

Technology has entered education quickly. In developed countries children and teachers have access to hundreds of thousands of learning applications and games. However, the digital divide is significant: some parts of the world still lack the basic requirements for participation in the digital revolution, such as electricity. Development of solar power and mobile technology will narrow this gap, rapidly revolutionizing the access to devices for technology-enabled education globally. One such area of advancements in technology is teacher education, where massive open online courses (MOOC) and other Internet sources offer today’s teachers the means to learn about the best pedagogies. Even though there is still a debate about the effectiveness of using educational technologies and the results have been inconclusive, the use of technology-enhanced learning (TEL) in education is increasing inevitably as the technologies get cheaper. At the same time, the rise in controlled intervention studies of TEL to support children and adults with MLD is offering new possibilities to understand the mechanisms of learning mathematics. During the last 10–15 years, the focus has been on different types of interventions to develop the number sense of the children with MLD. Slowly the interest is turning to more comprehensive models taking into account the core features of numerical understanding, the multiple concepts and representations in mathematics, and the cognitive skills needed in numerical processing, as well as the best pedagogical practices of special needs education.

Springer International Publishing2018

Emergency adaptation to distance learning: no magic bullet, but where there’s a will, there’s a way

Caroline Pulfrey

Whilst, in many countries, the sudden enforced transfer to large-scale distance learning in state schools as a result of COVID_19 lockdown measures caused turmoil, it also provided an unprecedented opportunity to understand more about key factors associated with successful uptake of distance learning in the context of national education systems. Past research on distance-learning has often been limited to specific contexts and functions, but the widespread nature of the pandemic lockdown meant that for a period of time, a majority pupils in public education were following distance education programs. In this research, we build on previous models of e-learning accessibility to create a holistic model of core determinants of distance-learning uptake. In this model outcome variables include teacher estimations of the percentage of their pupils following online learning programs during a six-week period and teacher perceptions of online teaching success. Predictor variables include three core dimensions: contextual factors (school infrastructure, equipment, number of pupils being taught and the school itself), individual factors (teacher and pupil competence, autonomy and motivation to work with distance learning) and pedagogic factors (tools and teaching methods used). The model was tested within the context of the Swiss national lockdown starting on March, 13th 2020 and lasting two months. Data was collected in one Swiss canton. An online survey was issued to all teachers in state education for pupils in primary and secondary schools. 5,666 teachers participated and, in addition, forty-five teachers were interviewed using semi-directed interview techniques. Participation was voluntary. Results of regression analyses carried out on the survey data, controlling for school-level effects, and backed by qualitative data from interviews, revealed two key and inter-related findings. Firstly, we saw that a wide range of factors contributed to lower rates of pupil drop-out and higher levels of teacher perceived success. These included elements of all three core dimensions. Contextual factors (equipment, communication, pupils’ family-organization, the degree of psychological and practical support provided to teachers by their schools) played a role, as did individual factors (the competence, experience and motivation of teachers themselves and their pupils). Likewise pedagogic factors (choice of tools for distance teaching and methods used to organize learning) had significant relations with estimated pupil uptake and teacher perceptions of success in distance teaching. The second main finding revealed that, while certain pedagogic tools such as video-conferencing with cameras on and messaging had a positive main effect on pupil uptake, the success of other tools, such as online collaborative platforms was moderated by teacher motivation, having a positive effect on pupil uptake when associated with teacher beliefs in the importance of maintaining contact and providing feedback to their pupils. The first result points to the need for a coherent, coordinated approach to distance learning that takes into account the multitude of factors relating to school, pupils, teachers, tools and methods. The second result underlines the fact that it is not just the tools teachers use for distance teaching that matter, but their intention behind the use and the resulting way they use the tools that makes the difference. Keywords: Large-scale distance learning, holistic model, teacher motivation, pedagogic tools, pedagogic methods

2022

Emergency adaptation to distance learning: no magic bullet, but where there’s a will, there’s a way

Caroline Pulfrey

2022