57,985 research outputs found
Computer Programming Effects in Elementary: Perceptions and Career Aspirations in STEM
The development of elementary-aged studentsâ STEM and computer science (CS) literacy is critical in this evolving technological landscape, thus, promoting success for college, career, and STEM/CS professional paths. Research has suggested that elementary- aged students need developmentally appropriate STEM integrated opportunities in the classroom; however, little is known about the potential impact of CS programming and how these opportunities engender positive perceptions, foster confidence, and promote perseverance to nurture studentsâ early career aspirations related to STEM/CS. The main purpose of this mixed-method study was to examine elementary-aged studentsâ (N = 132) perceptions of STEM, career choices, and effects from pre- to post-test intervention of CS lessons (N = 183) over a three-month period. Findings included positive and significant changes from studentsâ pre- to post-tests as well as augmented themes from 52 student interviews to represent increased enjoyment of CS lessons, early exposure, and its benefits for learning to future careers
Female Under-Representation in Computing Education and Industry - A Survey of Issues and Interventions
This survey paper examines the issue of female under-representation in computing education and industry, which has been shown from empirical studies to be a problem for over two decades. While various measures and intervention strategies have been implemented to increase the interest of girls in computing education and industry, the level of success has been discouraging.
The primary contribution of this paper is to provide an analysis of the extensive research work in this area. It outlines the progressive decline in female representation in computing education. It also presents the key arguments that attempt to explain the decline and intervention strategies. We conclude that there is a need to further explore strategies that will encourage young female learners to interact more with computer educational games
ScratchMaths: evaluation report and executive summary
Since 2014, computing has been part of the primary curriculum. âScratchâ is frequently used by schools, and the EEF funded this trial to test whether the platform could be used to improve pupilsâ computational thinking skills, and whether this in turn could have a positive impact on Key Stage 2 maths attainment. Good computational thinking skills mean pupils can use problem solving methods that involve expressing problems and their solutions in ways that a computer could execute â for example, recognising patterns. Previous research has shown that pupils with better computational thinking skills do better in maths.
The study found a positive impact on computational thinking skills at the end of Year 5 â particularly for pupils who have ever been eligible for free school meals. However, there was no evidence of an impact on Key Stage 2 maths attainment when pupils were tested at the end of Year 6.
Many of the schools in the trial did not fully implement ScratchMaths, particularly in Year 6, where teachers expressed concerns about the pressure of Key Stage 2 SATs. But there was no evidence that schools which did implement the programme had better maths results.
Schools may be interested in ScratchMaths as an affordable way to cover aspects of the primary computing curriculum in maths lessons without any adverse effect on core maths outcomes. This trial, however, did not provide evidence that ScratchMaths is an effective way to improve maths outcomes
Learning morphological phenomena of Modern Greek an exploratory approach
This paper presents a computational model for the description of concatenative morphological phenomena of modern Greek (such as inflection, derivation and compounding) to allow learners, trainers and developers to explore linguistic processes through their own constructions in an interactive openâended multimedia environment. The proposed model introduces a new language metaphor, the âpuzzleâmetaphorâ (similar to the existing âturtleâmetaphorâ for concepts from mathematics and physics), based on a visualized unificationâlike mechanism for pattern matching. The computational implementation of the model can be used for creating environments for learning through design and learning by teaching
A holistic model to infer mathematics performance: the interrelated impact of student, family and school context variables
The present study aims at exploring predictors influencing mathematics performance. In particular, the study focuses on internal students' characteristics (gender, age, metacognitive experience, mathematics self-efficacy) and external contextual factors (GDP of school location, parents' educational level, teachers' educational level, and teacher beliefs). A sample of 1749 students and 91 teachers from Chinese primary schools were involved in the study. Path analysis was used to test the direct and indirect relations between the predictors and mathematics performance. Results reveal that a large proportion of mathematics performance can be directly predicted from students' metacognitive experiences. In addition, other student characteristics and contextual variables influence mathematics performance in direct or indirect ways
The brain is a prediction machine that cares about good and bad - Any implications for neuropragmatics?
Experimental pragmatics asks how people construct contextualized meaning in communication. So what does it mean for this field to add neuroas a prefix to its name? After analyzing the options for any subfield of cognitive science, I argue that neuropragmatics can and occasionally should go beyond the instrumental use of EEG or fMRI and beyond mapping classic theoretical distinctions onto Brodmann areas. In particular, if experimental pragmatics âgoes neuroâ, it should take into account that the brain evolved as a control system that helps its bearer negotiate a highly complex, rapidly changing and often not so friendly environment. In this context, the ability to predict current unknowns, and to rapidly tell good from bad, are essential ingredients of processing. Using insights from non-linguistic areas of cognitive neuroscience as well as from EEG research on utterance comprehension, I argue that for a balanced development of experimental pragmatics, these two characteristics of the brain cannot be ignored
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