scholarly journals Game learning analytics for understanding reading skills in transparent writing system

2020 ◽  
Vol 51 (6) ◽  
pp. 2376-2390 ◽  
Author(s):  
Marko Niemelä ◽  
Tommi Kärkkäinen ◽  
Sami Äyrämö ◽  
Miia Ronimus ◽  
Ulla Richardson ◽  
...  
Author(s):  
Henry Brice ◽  
Noam Siegelman ◽  
Mark van den Bunt ◽  
Stephen J. Frost ◽  
Jay G. Rueckl ◽  
...  

Abstract Statistical learning (SL) approaches to reading maintain that proficient reading requires assimilation of rich statistical regularities in the writing system. Reading skills in developing first-language readers are predicted by individual differences in sensitivity to regularities in mappings from orthography to phonology (O-P) and semantics (O-S), where good readers rely more on O-P consistency, and less on O-S associations. However, how these regularities are leveraged by second-language (L2) learners remains an open question. We utilize an individual-differences approach, measuring L2 English learners’ sensitivity to O-P, O-S, and frequency during word-naming, across two years of immersion. We show that reliance on O-P is leveraged by better readers, while reliance on O-S is slower to develop, characterizing less proficient readers. All factors explain substantial individual variance in L2 reading skills. These findings show how SL plays a key role in L2 reading development through its role in assimilating sublexical regularities between print and speech.


Author(s):  
Jorge Alexander Aristizábal

  This paper shows how an American International School in Vietnam has been using data and Learning Analytics to learn about students learning from their assessment data and how to use these data to improve, among other areas, the reading skills of their mostly EAL student population. The source of data comes primarily from a Computer Adaptive Testing platform, commonly known as the MAP Growth test, which provides information about Math and Reading skills for each particular student. The data provided is transformed and presented to educational stakeholders through visualizations created in a specialized software in order to dig into the data and answer the pedagogical questions emerged from teachers and administrators. This process involves a new field know and Learning Analytics and Visual Data Mining in order to find new information not usually evident in school datasets. The results indicate that when teachers identify specific strengths and areas for improvement get into a reflective process that end up in actions plans for overall school and student learning improvement. In addition, learning analytics proves itself to be an effective way to understand what students learn and engage in actions to improve the conditions where learning happens.    


2020 ◽  
Vol 5 (4) ◽  
pp. 1026-1038
Author(s):  
Sandra Levey ◽  
Li-Rong Lilly Cheng ◽  
Diana Almodovar

Purpose The purpose of this review article is to present certain linguistic domains to consider in the assessment of children learning a new language. Speech-language pathologists frequently face difficulty when determining if a bilingual or multilingual child possesses a true speech or language disorder. Given the increased number of new language learners across the world, clinicians must understand differences versus disorders to prevent underidentification or overidentification of a disorder. Conclusions Early identification of a true disorder has been shown to prevent language and literacy difficulties, given that children are able to achieve grade-level reading skills when given intervention. Clinical knowledge and skills are strongly required so that children receive evidence-based assessment to support their academic development. Learning Goal Readers will gain an understanding of the factors that support evidence-based assessment of bilingual and multilingual language learners.


Author(s):  
Sandra Godinho ◽  
Margarida V. Garrido ◽  
Oleksandr V. Horchak

Abstract. Words whose articulation resembles ingestion movements are preferred to words mimicking expectoration movements. This so-called in-out effect, suggesting that the oral movements caused by consonantal articulation automatically activate concordant motivational states, was already replicated in languages belonging to Germanic (e.g., German and English) and Italic (e.g., Portuguese) branches of the Indo-European family. However, it remains unknown whether such preference extends to the Indo-European branches whose writing system is based on the Cyrillic rather than Latin alphabet (e.g., Ukrainian), or whether it occurs in languages not belonging to the Indo-European family (e.g., Turkish). We replicated the in-out effect in two high-powered experiments ( N = 274), with Ukrainian and Turkish native speakers, further supporting an embodied explanation for this intriguing preference.


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