The Changing Nature of School Library Collections

published or submitted for publicatio

Special Libraries, August 1980

Volume 71, Issue 8https://scholarworks.sjsu.edu/sla_sl_1980/1006/thumbnail.jp

Analysis of the pedagogical perspective of the MOOCs available in Portuguese

After an initial stage of exponential growth in MOOCs, a need has arisen of to address several different aspects of these innovations in order to understand and develop them from different perspectives, such as this one, with the analysis of pedagogical dimensions aimed at improving course design. This paper presents an updated review of the literature and proposes five research lines for an in-depth approach. This study is part of a broader research project1 and here analyses 356 MOOCs delivered in Portuguese by 16 different platforms. The research design is quantitative, non-experimental and transversal. An adaptation of the MOOC Educational and Interactive Indicators Instrument —INdiMOOC-EdI— was used in the data collection process. The reliability and internal consistency analysis of that adaptation for the whole sample resulted in a Cronbach alpha score of 0.731. The data obtained enable us to classify the existing MOOCs in Portuguese according to descriptive, formative, and interactive components. These different types correlate with the quality indices, being negative in the first dimension (descriptive) and positive in the second and third ones (formative and interactive).Funded by the call for R&D&i projects named: «Estudio del impacto de las erubricas federada en evaluación de las competencias en el practicum» (Study on the impact of federated eRubrics in the evaluation of the competences in the practicum). Plan Nacional de I+D+i de Excelencia (National R&D&i Excellence Plan) (2014-16) no. EDU2013-41974-

System development guidelines from a review of motion-based technology for people with MCI or dementia

As the population ages and the number of people living with dementia or mild cognitive impairment (MCI) continues to increase, it is critical to identify creative and innovative ways to support and improve their quality of life. Motion-based technology has shown significant potential for people living with dementia or MCI by providing opportunities for cognitive stimulation, physical activity and participation in meaningful leisure activities, while simultaneously functioning as a useful tool for research and development of interventions. However, many of the current systems created using motion-based technology have not been designed specifically for people with dementia or MCI. Additionally, the usability and accessibility of these systems for these populations has not been thoroughly considered. This paper presents a set of system development guidelines derived from a review of the state of the art of motion-based technologies for people with dementia or MCI. These guidelines highlight three overarching domains of consideration for systems targeting people with dementia or MCI: (i) cognitive, (ii) physical, and (iii) social. We present the guidelines in terms of relevant design and use considerations within these domains and the emergent design themes within each domain. Our hope is that these guidelines will aid in designing motion-based software to meet the needs of people with dementia or MCI such that the potential of these technologies can be realized

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior