Children's interactions with interactive toy technology

Abstract Digital toys offer the opportunity to explore software scaffolding through tangible interfaces that are not bound to the desktop computer. This paper describes the empirical work completed by the CACHET (Computers and Children's Electronic Toys) project team investigating young children's use of interactive toy technology. The interactive toys in question are plush and cuddly cartoon characters with embedded sensors that can be squeezed to evoke spoken feedback from the toy. In addition to playing with the toy as it stands, the toy can be linked to a desktop PC with compatible software using a wireless radio connection. Once this connection is made the toy offers hints and tips to the children as they play with the accompanying software games. If the toy is absent, the same hints and tips are available through an on-screen animated icon of the toy's cartoon character. The toys as they stand are not impressive as collaborative learning partners, as their help repertoire is inadequate and even inappropriate. However, the technology has potential: children can master the multiple interfaces of toy and screen and, when the task requires it and the help provided is appropriate, they will both seek and use it. In particular, the cuddly interface experience can offer an advantage and the potential for fun interfaces that might address both the affective and the effective dimensions of learners' interactions

Exploring the community waste sector: Are sustainable development and social capital useful concepts for project-level research?

The concept of sustainable development implies that social, economic and environmental objectives should be delivered together, and that they can be achieved through enhanced community participation. The concept of social capital indicates how these objectives interrelate, implying that community involvement enhances trust and reciprocity, thus promoting better governance and greater prosperity. This paper draws on a survey of Community Waste Projects to explore how these concepts can inform investigations of community projects. It argues that the concepts provide useful guides to research and debate, but highlights the resource requirements of empirically confirming the claims of the social capital perspective

Initiating e-learning by stealth, participation and consultation in a late majority institution

The extent to which opportunities afforded by e-learning are embraced by an institution can depend in large measure on whether it is perceived as enabling and transformative or as a major and disruptive distraction. Most case studies focus on the former. This paper describes how e-learning was introduced into the latter environment. The sensitivity of competing pressures in a research intensive university substantially influenced the manner in which e-learning was promoted. This paper tells that story, from initial stealth to eventual university acknowledgement of the relevance of e-learning specifically to its own context

Up and down the number line: modelling collaboration in contrasting school and home environments

This paper is concerned with user modelling issues such as adaptive educational environments, adaptive information retrieval, and support for collaboration. The HomeWork project is examining the use of learner modelling strategies within both school and home environments for young children aged 5 – 7 years. The learning experience within the home context can vary considerably from school especially for very young learners, and this project focuses on the use of modelling which can take into account the informality and potentially contrasting learning styles experienced within the home and school

Making it real: exploring the potential of Augmented Reality for teaching primary school science

The use of Augmented Reality (AR) in formal education could prove a key component in future learning environments that are richly populated with a blend of hardware and software applications. However, relatively little is known about the potential of this technology to support teaching and learning with groups of young children in the classroom. Analysis of teacher-child dialogue in a comparative study between use of an AR virtual mirror interface and more traditional science teaching methods for 10-year-old children, revealed that the children using AR were less engaged than those using traditional resources. We suggest four design requirements that need to be considered if AR is to be successfully adopted into classroom practice. These requirements are: flexible content that teachers can adapt to the needs of their children, guided exploration so learning opportunities can be maximised, in a limited time, and attention to the needs of institutional and curricular requirements

Current Understanding, Support Systems, and Technology-led Interventions for Specific Learning Difficulties

In January 2019, the Government Office for Science commissioned a series of 4 rapid evidence reviews to explore how technology and research can help improve educational outcomes for learners with Specific Learning Difficulties (SpLDs). This review examined: 1) current understanding of the causes and identification of SpLDs, 2)the support system for learners with SpLDs, 3)technology-based interventions for SpLDs 4) a case study approach focusing on dyscalculia to explore all 3 theme

Machine and human observable differences in groups’ collaborative problem-solving behaviours

This paper contributes to our understanding of how to design learning analytics to capture and analyse collaborative problem-solving (CPS) in practice-based learning activities. Most research in learning analytics focuses on student interaction in digital learning environments, yet still most learning and teaching in schools occurs in physical environments. Investigation of student interaction in physical environments can be used to generate observable differences among students, which can then be used in the design and implementation of Learning Analytics. Here, we present several original methods for identifying such differences in groups CPS behaviours. Our data set is based on human observation, hand position (fiducial marker) and heads direction (face recognition) data from eighteen students working in six groups of three. The results show that the high competent CPS groups spend an equal distribution of time on their problem-solving and collaboration stages. Whereas, the low competent CPS groups spend most of their time in identifying knowledge and skill deficiencies only. Moreover, as machine observable data shows, high competent CPS groups present symmetrical contributions to the physical tasks and present high synchrony and individual accountability values. The findings have significant implications on the design and implementation of future learning analytics systems

Early Dropout Prediction for Programming Courses Supported by Online Judges

Many educational institutions have been using online judges in programming classes, amongst others, to provide faster feedback for students and to reduce the teacher’s workload. There is some evidence that online judges also help in reducing dropout. Nevertheless, there is still a high level of dropout noticeable in introductory programming classes. In this sense, the objective of this work is to develop and validate a method for predicting student dropout using data from the first two weeks of study, to allow for early intervention. Instead of the classical questionnaire-based method, we opted for a non-subjective, data-driven approach. However, such approaches are known to suffer from a potential overload of factors, which may not all be relevant to the prediction task. As a result, we reached a very promising 80% of accuracy, and performed explicit extraction of the main factors leading to student dropout

Defining a self-evaluation digital literacy framework for secondary educators: the DigiLit Leicester project

Despite the growing interest in digital literacy within educational policy, guidance for secondary educators in terms of how digital literacy translates into the classroom is lacking. As a result, many teachers feel ill-prepared to support their learners in using technology effectively. The DigiLit Leicester project created an infrastructure for holistic, integrated change, by supporting staff development in the area of digital literacy for secondary school teachers and teaching support staff. The purpose of this article is to demonstrate how the critique of existing digital literacy frameworks enabled a self-evaluation framework for practitioners to be developed. Crucially, this framework enables a co-operative, partnership approach to be taken to pedagogic innovation. Moreover, it enables social and ethical issues to underpin a focus on teacher-agency and radical collegiality inside the domain of digital literacy. Thus, the authors argue that the shared development framework constitutes a new model for implementing digital literacy aimed at transforming the provision of secondary education across a city

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in