Browsing by Subject "K-12"

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Open Access
Block-based coding in K-12 education: a systematic literature review
(2022-02) Köksaloğlu, Cemre
This study aims to determine the methodological tendencies and outputs of the studies conducted on the use of block-based coding tools in K-12 education by systematic literature review. According to their demographic characteristics, a total of 59 full-text research articles were analyzed through thematic content analysis. In addition, learning domains were explored in the research articles, and results were revealed by themes regarding the impact of block-based coding in K-12 education on learning domains. Research findings display that most of the research articles were published in 2019, commonly using the mixed-method research, and they were carried out with middle school level students with less than 50 sample sizes. This review also found that most research studies were conducted in the United States and in the Computer Science subject area. Finally, the most frequently used block-based coding tool was Scratch and problem-based learning approach was the most used considering the teaching/learning approaches. Besides, the cognitive domain was explored mainly in the research articles. The results of the research articles indicated that block-based coding tools improve the following skills regarding cognitive abilities: computational thinking, problem-solving, algorithmic and reflective thinking, critical thinking, and creative thinking skills. Moreover, results for interdisciplinary learning and gender differences regarding the cognitive domain were also found. Additionally, block-based coding positively affected students' feelings regarding the affective domain. Lastly, whereas students had positive attitudes towards coding, negative attitudes were also reported.
Open Access
Improving educational web search for question-like queries through subject classification
(Elsevier, 2019) Yılmaz, Tolga; Özcan, R.; Altıngövde, İ. Ş; Ulusoy, Özgür
Students use general web search engines as their primary source of research while trying to find answers to school-related questions. Although search engines are highly relevant for the general population, they may return results that are out of educational context. Another rising trend; social community question answering websites are the second choice for students who try to get answers from other peers online. We attempt discovering possible improvements in educational search by leveraging both of these information sources. For this purpose, we first implement a classifier for educational questions. This classifier is built by an ensemble method that employs several regular learning algorithms and retrieval based approaches that utilize external resources. We also build a query expander to facilitate classification. We further improve the classification using search engine results and obtain 83.5% accuracy. Although our work is entirely based on the Turkish language, the features could easily be mapped to other languages as well. In order to find out whether search engine ranking can be improved in the education domain using the classification model, we collect and label a set of query results retrieved from a general web search engine. We propose five ad-hoc methods to improve search ranking based on the idea that the query-document category relation is an indicator of relevance. We evaluate these methods for overall performance, varying query length and based on factoid and non-factoid queries. We show that some of the methods significantly improve the rankings in the education domain.