Perception and knowledge of learners about the use of 3D technologies in manual therapy education - a qualitative study.

OBJECTIVES: Manual therapy is a specific hands-on approach used and taught by various professions such as physiotherapy and osteopathy. The current paradigm of teaching manual therapy incorporates the traditional 'See one, do one, teach one' approach. However, this 'teacher centred' approach may not enable learners to develop the complex clinical skills of manual therapy. In this context, 3D technologies such as virtual reality may facilitate the teaching and learning of manual therapy. Hence the aim of the current study was to investigate the perception, knowledge and attitude of manual therapy learners about the use of 3D technologies in manual therapy education. METHODS: An exploratory qualitative research design using semi-structured interviews was used in this study. A total of ten manual therapy (5 physiotherapy and 5 osteopathic) students (mean age = 32; 80% female) enrolled in an appropriate physiotherapy or osteopathic degree provided by a New Zealand recognized institution (e.g., university or polytechnic) participated in this study. Data saturation was achieved after 10 interviews (average duration: 35 min) that provided thick data. A thematic analysis was used for data analysis. RESULTS: Six factors were identified which appeared to influence participants' perception of role of technology in manual therapy education. These were (1) the sufficiency of current teaching method; (2) evolution as a learner (a novice to an expert); (3) need for objectivity; (4) tutor feedback; (5) knowledge and (6) barriers and enablers. These six factors influenced the participants' perception about the role of 3D technologies in manual therapy education with participants evidently taking two distinct/polarized positions ('no role' (techstatic) versus a 'complete role' (techsavvy)). CONCLUSION: Although 3D technology may not replace face-to-face teaching, it may be used to complement the traditional approach of learning/teaching to facilitate the learning of complex skills according to the perceptions of manual therapy learners in our study. The advantage of such an approach is an area of future research.

Arabic punctuation dataset.

Arabic, unlike many languages, suffers from punctuation inconsistency, posing a significant obstacle for Natural Language Processing (NLP). To address this, we present the Arabic Punctuation Dataset (APD), a large collection of annotated Modern Standard Arabic texts designed to train machine learning models in sentence boundary identification and punctuation prediction. APD leverages the "theme-rheme completion" principle, a grammatical feature closely linked to consistent punctuation placement. It consists of an annotated collection of Modern Standard Arabic (MSA) texts that encompass 312 million words in approximately 12 million sentences. It comprises three diverse components: Arabic Book Chapters (ABC): Manually annotated, non-fiction, book excerpts, constituting a gold-standard reference. Complete Book Translations (CBT): Parallel English-Arabic book translations with aligned sentence endings, ideal for machine translation training. Scrambled Sentences from the Arabic Component of the United Nations Parallel Corpus (SSAC-UNPC): Jumbled sentences for model training in automatic punctuation restoration. Beyond NLP, APD serves as a valuable resource for linguistics research, language learning, and real-time subtitling. Its authentic, grammar-based approach can enhance the readability and clarity of machine-generated text, opening doors for various applications such as automatic speech recognition, text summarization, and machine translation.