526 research outputs found
Thematic Working Group 3 - Inclusion of Excluded Populations : Access and Learning Optimization via IT in the Post-Pandemic Era
Thematic Working Group (TWG) 3âs theme is âInclusion of excluded populations: access and learning optimization via IT in the post-pandemic eraâ. A focal concern is established by the presence of the first word â âinclusionâ â and how this relates to âexcluded populationsâ. Much of the research in this field has focused on inclusion for individuals; however, the evidence shows that educational exclusion has multiple dimensions (Passey, 2014). To accommodate this within the current focus, therefore, identifying key dimensions of âexcluded populationsâ will be a key concern of this document. âAccessâ will be considered beyond physical technology access, involving aspects of accessibility, agency and empowerment. These aspects relate to a definition of access that concerns the needs for individuals to develop and have digital capabilities and abilities to select applications appropriate to purpose, as discussed, for example, by Helsper (2021) and Passey et al. (2018). Taking this wider concern for access, âlearning optimizationâ will be explored as a term that highlights the need to focus on technological access and provision enabling successful outcomes. Given the fact that the intention of the work of TWG3 is to explore findings in the âpost-pandemicâ context, communication technologies as well as just information technology, âITâ, are clearly important and need to be considered. Additionally, exclusion factors to be addressed need to be clearly identified so that inclusion can be accommodated and ensured in the context of specific excluded populations. However, inclusion should not be implemented as an imposition in the context of digital technologies, as some populations do not wish to use digital technologies (Wetmore, 2007), and in this respect the issue of the need to acknowledge diversity is important
Dynamic assessment of the effectiveness of digital game-based literacy training in beginning readers:a cluster randomised controlled trial
In this article, we report on a study evaluating the effectiveness of a digital game-based learning (DGBL) tool for beginning readers of Dutch, employing active (math game) and passive (no game) control conditions. This classroom-level randomized controlled trial included 247 first graders from 16 classrooms in the Netherlands and the Dutch-speaking part of Belgium. The intervention consisted of 10 to 15 min of daily playing during school time for a period of up to 7 weeks. Our outcome measures included reading fluency, phonological skills, as well as purpose built in-game proficiency levels to measure written lexical decision and letter speech sound association. After an average of 28 playing sessions, the literacy game improved letter knowledge at a scale generalizable for all children in the classroom compared to the two control conditions. In addition to a small classroom wide benefit in terms of reading fluency, we furthermore discovered that children who scored high on phonological awareness prior to training were more fluent readers after extensive exposure to the reading game. This study is among the first to exploit game generated data for the evaluation of DGBL for literacy interventions
A Causal-Comparative Investigation of the Effect of Middle School Teachersâ Perceptions of Studentsâ Socioeconomic Status on their Attitudes Toward Technology
This quantitative causal-comparative study investigated the effect of middle school teachersâ perceptions of studentsâ socioeconomic status on their attitudes toward technology. The study was based on the theory of social constructivism and the will, skill, and tool model of technology integration to investigate teachersâ attitudes toward technology. This study advanced the body of knowledge by examining the connection between pedagogical beliefs and teachersâ attitudes toward technology, the use of technology with students from lower socioeconomic backgrounds, and the need for more research on technology use by teachers at the middle school level. The research question exploring the possibility of a difference in teachersâ attitudes toward technology among middle school teachers who minimally, somewhat, and predominantly serve students from low socioeconomic backgrounds, as determined by the state and United States Department of Education, was measured by the Teachers\u27 Attitudes Toward Computers-Information Computer Technology Questionnaire (TAC/TAICT) using responses from 126 middle school teachers in Virginia. The researcher collected data through digital completion of the questionnaire and analyzed it to determine significant differences. A one-way ANOVA did not show significant differences in overall attitudes among the three groups
Adaptive Automated Machine Learning
The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary. Automated Machine Learning tackles the problem of finding machine learning models with high predictive performance. Existing approaches incorporating deep learning techniques assume that all data is available at the beginning of the training process (offline learning). They configure and optimise a pipeline of preprocessing, feature engineering, and model selection by choosing suitable hyperparameters in each model pipeline step. Furthermore, they assume that the user is fully aware of the choice and, thus, the consequences of the underlying metric (such as precision, recall, or F1-measure). By variation of this metric, the search for suitable configurations and thus the adaptation of algorithms can be tailored to the userâs needs. With the creation of a vast amount of data from all kinds of sources every day, our capability to process and understand these data sets in a single batch is no longer viable. By training machine learning models incrementally (i.ex. online learning), the flood of data can be processed sequentially within data streams. However, if one assumes an online learning scenario, where an AutoML instance executes on evolving data streams, the question of the best model and its configuration remains open.
In this work, we address the adaptation of AutoML in an offline learning scenario toward a certain utility an end-user might pursue as well as the adaptation of AutoML towards evolving data streams in an online learning scenario with three main contributions:
1. We propose a System that allows the adaptation of AutoML and the search for neural architectures towards a particular utility an end-user might pursue.
2. We introduce an online deep learning framework that fosters the research of deep learning models under the online learning assumption and enables the automated search for neural architectures.
3. We introduce an online AutoML framework that allows the incremental adaptation of ML models.
We evaluate the contributions individually, in accordance with predefined requirements and to state-of-the- art evaluation setups. The outcomes lead us to conclude that (i) AutoML, as well as systems for neural architecture search, can be steered towards individual utilities by learning a designated ranking model from pairwise preferences and using the latter as the target function for the offline learning scenario; (ii) architectual small neural networks are in general suitable assuming an online learning scenario; (iii) the configuration of machine learning pipelines can be automatically be adapted to ever-evolving data streams and lead to better performances
Educational Data Mining to Predict Bachelors Studentsâ Success
Predicting academic success is essential in higher education because it is perceived as a critical driver for scientific and technological advancement and countriesâ economic and social development. This paper aims to retrieve the most relevant attributes for academic success by applying educational data mining (EDM) techniques to a Portuguese business school bachelorâs historical data. We propose two predictive models to classify each student regarding academic success at enrolment and the end of the first academic year. We implemented a SEMMA methodology and tried several machine learning algorithms, including decision trees, KNN, neural networks, and SVM. The best classifier for academic success at the entry-level reached is a random forest with an accuracy of 69%. At the end of the first academic year, an MLP artificial neural networkâs best performance was achieved with an accuracy of 85%. The main findings show that at enrolment or the end of the first year, the grades and, thus, the studentâs previous education and engagement with the school environment are decisive in achieving academic success. Doi: 10.28991/ESJ-2023-SIED2-013 Full Text: PD
Responsible AI in Africa
This open access book contributes to the discourse of Responsible Artificial Intelligence (AI) from an African perspective. It is a unique collection that brings together prominent AI scholars to discuss AI ethics from theoretical and practical African perspectives and makes a case for African values, interests, expectations and principles to underpin the design, development and deployment (DDD) of AI in Africa. The book is a first in that it pays attention to the socio-cultural contexts of Responsible AI that is sensitive to African cultures and societies. It makes an important contribution to the global AI ethics discourse that often neglects AI narratives from Africa despite growing evidence of DDD in many domains. Nine original contributions provide useful insights to advance the understanding and implementation of Responsible AI in Africa, including discussions on epistemic injustice of global AI ethics, opportunities and challenges, an examination of AI co-bots and chatbots in an African work space, gender and AI, a consideration of African philosophies such as Ubuntu in the application of AI, African AI policy, and a look towards a future of Responsible AI in Africa. This is an open access book
Dynamic assessment of the effectiveness of digital game-based literacy training in beginning readers: a cluster randomised controlled trial
In this article, we report on a study evaluating the effectiveness of a digital game-based learning (DGBL) tool for beginning readers of Dutch, employing active (math game) and passive (no game) control conditions. This classroom-level randomized controlled trial included 247 first graders from 16 classrooms in the Netherlands and the Dutch-speaking part of Belgium. The intervention consisted of 10 to 15 min of daily playing during school time for a period of up to 7 weeks. Our outcome measures included reading fluency, phonological skills, as well as purpose built in-game proficiency levels to measure written lexical decision and letter speech sound association. After an average of 28 playing sessions, the literacy game improved letter knowledge at a scale generalizable for all children in the classroom compared to the two control conditions. In addition to a small classroom wide benefit in terms of reading fluency, we furthermore discovered that children who scored high on phonological awareness prior to training were more fluent readers after extensive exposure to the reading game. This study is among the first to exploit game generated data for the evaluation of DGBL for literacy interventions
A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions
In recent decades, social network anonymization has become a crucial research
field due to its pivotal role in preserving users' privacy. However, the high
diversity of approaches introduced in relevant studies poses a challenge to
gaining a profound understanding of the field. In response to this, the current
study presents an exhaustive and well-structured bibliometric analysis of the
social network anonymization field. To begin our research, related studies from
the period of 2007-2022 were collected from the Scopus Database then
pre-processed. Following this, the VOSviewer was used to visualize the network
of authors' keywords. Subsequently, extensive statistical and network analyses
were performed to identify the most prominent keywords and trending topics.
Additionally, the application of co-word analysis through SciMAT and the
Alluvial diagram allowed us to explore the themes of social network
anonymization and scrutinize their evolution over time. These analyses
culminated in an innovative taxonomy of the existing approaches and
anticipation of potential trends in this domain. To the best of our knowledge,
this is the first bibliometric analysis in the social network anonymization
field, which offers a deeper understanding of the current state and an
insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure
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