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Computer-assisted instruction (CAI) has been used to combat reading challenges, namely reading accuracy and rate for learners with intellectual, developmental and learning disabilities (IDLD). Whilst most reading CAI effectiveness has been studied in English, other transparent languages have less evidence. This study provides a systematic review and meta-analysis of CAI effectiveness for transparent language reading for K-3 learners with IDLD.

This study systematically reviews academic peer-reviewed studies from 2010 to 2023 with either randomised controlled treatment (RCT) or single-case treatments. Articles were searched from the ACM Digital Library, Google Scholar, IEEE Xplore, ERIC, PsychINFO and Science Direct databases, references and systematic review articles. Reading component skills effect sizes were computed using the random effect sizes model.

11 RCT studies of reading CAI for transparent languages with 510 learners with IDLD were found. A random effect sizes (Cohen’s d) of CAI on individual reading component skills were d = 0.24, p-value = 0.063 and confidence interval (CI) 95% (−0.068–0.551) for phonics and phonemic awareness d = 0.41, p-value = 0.000 and CI 95% (0.175–0.644). Given an average intervention dosage of 1.8 h weekly for a maximum of 16 weeks, CAI had better retention with d = 1.13, p-value = 0.066 and CI 95%(−0.339–2.588). However, these results must be interpreted with a concern of only using published studies.

The study contributes to quantitative CAI effectiveness for transparent language reading components for learners with IDLD.

Introduction: The word consists of different phonemes, which are interconnected to form a meaningful word, since phonemes as a single phoneme have no meaning, but their union forms the meaning of the word.

Purpose: Since the phonemes in the Albanian language, both vowels and consonants, have contradictions with each other, manage to bring words that have the same etymology but can change during writing. For example, in Gheg dialect – baj; hanë, while in Tosk dialect – bëj; hënë, etc. Therefore, the aim of this chapter is to highlight the characteristics of the phonemes of the Albanian language, accordingly the use of phonetic and dialectal elements in the Balkans, before and at present, since these may have implications for economic and industrial strategies.

Need for the study: The Albanian language has a rich vocabulary with standard speech, but its dialects have more words than standard speech. Each dialectal change has required study to see the cause of the change in these phonemes, vowels and consonants.

Methodology: The chapter is focussed on the descriptive method of research. Therefore, the comparison of research approaches has been considered. A quantitative deductive method has been applied for data analyses.

Findings: The results reveal that some of the phonemes undergo changes over time spam. The changes occur in the two dialects of Albania, Gheg and Tosk.

Four Albanian vowels, the phonemes /a/, /e/, /i/ and /u/, come directly from vowels inherited from the Indo-European period; the vowel /o/ is formed by the evolution of long vowels; while the vowels /y/ and /ë/ were found from other vowels in the internal developments of the Albanian language. Whereas nasal vowels were also formed later as internal developments of the language.

Practical implications: The study is important for language researchers and affects the appearance of elements from the field of dialectology and phonetics.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.