Hasil untuk "Special aspects of education"

Wenbo Li, Ziyao Ding, Shuo Feng et al.

Abstract Background Traditional lecture-based learning (LBL) faces limitations in teaching complex spinal anatomy and surgical procedures. This study aimed to evaluate the efficacy of a novel Problem-Based Learning (PBL) model integrated with three-dimensional (3D) anatomy software and software-assisted annotation in spinal surgery education. Methods A randomized controlled trial included 120 fifth-year clinical medicine undergraduates, starting in August 2024. Participants were divided into an experimental group (n = 60, receiving 3D + PBL + annotation-assisted teaching) and a control group (n = 60, receiving traditional LBL). Outcomes were assessed via written tests (objective/subjective questions) and standardized questionnaires evaluating knowledge mastery, learning motivation, academic atmosphere, teacher-student interaction, and knowledge retention. Results The experimental group scored significantly higher on subjective questions (case analysis) than the control group (39.33 ± 5.38 vs. 32.08 ± 4.79, P < 0.001). Questionnaire results indicated that the experimental group reported significantly higher self-rated mastery in spinal endoscopic procedures, anatomy, Michigan State University (MSU) classification of lumbar disc herniation (LDH), and differential diagnosis (all P < 0.05). In addition, students in the experimental group expressed greater satisfaction with learning motivation, academic atmosphere, teacher–student interaction, and knowledge retention (all P < 0.05). Conclusions Integrating 3D anatomy visualization, software-assisted annotation, and PBL significantly enhances clinical reasoning, spatial understanding, and student engagement in spinal surgery education. This multimodal approach addresses the limitations of traditional methods and is recommended for broader application in orthopedic training. Trial registration Chinese Clinical Trial Registry (ChiCTR), ChiCTR2400082568. Registered on 01 April 2024.

Peng Yang, Yunfeng Zhu, Chao Chang et al.

The rapid integration of Large Language Models (LLMs) into software engineering practice is reshaping how software testing activities are performed. LLMs are increasingly used to support software testing. Consequently, software testing education must evolve to prepare students for this new paradigm. However, while students have already begun to use LLMs in an ad hoc manner for testing tasks, there is limited empirical understanding of how such usage influences their testing behaviors, judgment, and learning outcomes. It is necessary to conduct a systematic investigation into how students learn to evaluate, control, and refine LLM-assisted testing results. This paper presents a mixed-methods, two-phase exploratory study on human-LLM collaboration in software testing education. In Phase I, we analyze classroom learning artifacts and interaction records from 15 students, together with a large-scale survey conducted in a national software testing competition (337 valid responses), to identify recurring prompt-related difficulties across testing tasks. The results reveal systematic interaction breakdowns, including missing contextual information, insufficient constraints, rigid one-shot prompting, and limited strategy-driven iteration, with automated test script generation emerging as a particularly heterogeneous and effort-intensive interaction context. Building on these findings, Phase II conducts an illustrative classroom practice that operationalizes the observed breakdowns into a lightweight, stage-aware prompt scaffold for test script generation, guiding students to explicitly articulate execution-relevant information such as environmental assumptions, interaction grounding, synchronization, and validation intent, and reporting descriptive shifts in students' testing-related articulation when interacting with LLMs.

Tryfon Sivenas, Panagiota Maragkaki

The purpose of this study was to examine, from the perspective of secondary education students, the educational affordances and constraints of using Artificial Intelligence (AI) in teaching and learning. The sample consisted of 45 students from the 2nd year of General Lyceum (11th grade, ages 16-17) in Greece, who, after becoming familiarized with ChatGPT-4o and completing six activities, filled in an open-ended questionnaire related to the research purpose. Open, axial, and selective coding of the data revealed that students recognize five educational affordances: the creation of new knowledge building on prior knowledge, immediate feedback, friendly interaction through messaging, ease and speed of access to information, and skills development. Concurrently, three main constraints were identified: content reliability, anxiety about AI use, and privacy concerns. The study concludes that students are positive toward AI use in education.

Yuli Widyaningsih, Ikhlasul Ardi Nugroho

This study explores the impact of reading motivation and academic performance among third-grade students. The problem addressed in this research is the limited understanding of how reading motivation influences students' performance, particularly in the context of early education. The background highlights the importance of intrinsic motivation in learning, with previous studies indicating that motivated students are more likely to engage with educational materials and perform better academically. This study employs a quantitative approach, using both univariate and bivariate analyses to assess the distribution of reading motivation and its effect on academic performance. A total of 27 third-grade students were surveyed on their reading motivation, and their academic performance was analyzed. The findings reveal that 59.3% of students exhibited high reading motivation, with 63% showing high academic performance. The logistic regression analysis further shows a significant association between reading motivation and performance (p-value = 0.004), with students exhibiting high motivation more likely to perform well academically. This research contributes to the growing body of literature on the role of motivation in educational outcomes, emphasizing the need for fostering intrinsic reading motivation to improve academic performance. It also provides practical implications for educators and policymakers, encouraging the creation of supportive environments that promote reading engagement and motivation.

Sven Jacobs, Natalie Kiesler

Real-time voice interfaces using multimodal Generative AI (GenAI) can potentially address the accessibility needs of novice programmers with disabilities (e.g., related to vision). Yet, little is known about how novices interact with GenAI tools and their feedback quality in the form of audio output. This paper analyzes audio dialogues from nine 9th-grade students using a voice-enabled tutor (powered by OpenAI's Realtime API) in an authentic classroom setting while learning Python. We examined the students' voice prompts and AI's responses (1210 messages) by using qualitative coding. We also gathered students' perceptions via the Partner Modeling Questionnaire. The GenAI Voice Tutor primarily offered feedback on mistakes and next steps, but its correctness was limited (71.4% correct out of 416 feedback outputs). Quality issues were observed, particularly when the AI attempted to utter programming code elements. Students used the GenAI voice tutor primarily for debugging. They perceived it as competent, only somewhat human-like, and flexible. The present study is the first to explore the interaction dynamics of real-time voice GenAI tutors and novice programmers, informing future educational tool design and potentially addressing accessibility needs of diverse learners.

Antoun Yaacoub, Sansiri Tarnpradab, Phattara Khumprom et al.

Artificial intelligence (AI) is rapidly transforming education, presenting unprecedented opportunities for personalized learning and streamlined content creation. However, realizing the full potential of AI in educational settings necessitates careful consideration of the quality, cognitive depth, and ethical implications of AI-generated materials. This paper synthesizes insights from four related studies to propose a comprehensive framework for enhancing AI-driven educational tools. We integrate cognitive assessment frameworks (Bloom's Taxonomy and SOLO Taxonomy), linguistic analysis of AI-generated feedback, and ethical design principles to guide the development of effective and responsible AI tools. We outline a structured three-phase approach encompassing cognitive alignment, linguistic feedback integration, and ethical safeguards. The practical application of this framework is demonstrated through its integration into OneClickQuiz, an AI-powered Moodle plugin for quiz generation. This work contributes a comprehensive and actionable guide for educators, researchers, and developers aiming to harness AI's potential while upholding pedagogical and ethical standards in educational content generation.

Lukas Adriel Francisco Alves, Maria Marta da Silva

Este artigo tem como objetivo principal investigar como uma situação desencadeadora de aprendizagem acerca do conceito de número contribuiu para o entendimento do movimento lógico-histórico, como proposta para a organização do ensino de conceitos matemáticos. O Clube de Matemática da Universidade Estadual de Goiás, Campus Sudoeste, Sede Quirinópolis, foi o espaço formativo que abrigou as ações da pesquisa. Nesse contexto, durante o período de 2017 a 2023, aproximadamente trinta professores de Matemática em formação participaram de um experimento formativo que buscou respostas a seguinte questão problematizadora: Quais são as contribuições formativas que a organização do ensino de conceitos matemáticos ofertada pelo Clube de Matemática, a partir do movimento lógico-histórico, pode trazer a professores de Matemática em formação inicial? Os resultados dão indícios de que os sujeitos compreenderam os conceitos matemáticos como produções humanas que materializam respostas às necessidades postas na realidade objetiva. Também indicam a necessidade de professores e de alunos terem a oportunidade de aprender os conceitos matemáticos a partir de um modelo geral de ações que privilegie o movimento lógico-histórico.

Editorial Team

Nicy Scaria, Suma Dharani Chenna, Deepak Subramani

Developing questions that are pedagogically sound, relevant, and promote learning is a challenging and time-consuming task for educators. Modern-day large language models (LLMs) generate high-quality content across multiple domains, potentially helping educators to develop high-quality questions. Automated educational question generation (AEQG) is important in scaling online education catering to a diverse student population. Past attempts at AEQG have shown limited abilities to generate questions at higher cognitive levels. In this study, we examine the ability of five state-of-the-art LLMs of different sizes to generate diverse and high-quality questions of different cognitive levels, as defined by Bloom's taxonomy. We use advanced prompting techniques with varying complexity for AEQG. We conducted expert and LLM-based evaluations to assess the linguistic and pedagogical relevance and quality of the questions. Our findings suggest that LLms can generate relevant and high-quality educational questions of different cognitive levels when prompted with adequate information, although there is a significant variance in the performance of the five LLms considered. We also show that automated evaluation is not on par with human evaluation.

Wei Hung Pan, Ming Jie Chok, Jonathan Leong Shan Wong et al.

Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct. In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were obtained from various sources, including 80 from Quescol, 3,264 from Kaggle, and 1,725 from LeetCode. From the dataset, we created 13 sets of code problem variant prompts, which were used to instruct ChatGPT to generate the outputs. Subsequently, we assessed the performance of five AIGC detectors. Our results demonstrate that existing AIGC Detectors perform poorly in distinguishing between human-written code and AI-generated code.

Shams El-Adawy, Cydney Alexis, Eleanor C. Sayre

Various motivations bring researchers to discipline-based education research (DBER), but there is little research on their conceptualization of and navigation into this new-to-them area of research. We use phenomenography to analyze interview data collected from twenty-eight emerging STEM education researchers to gain a better understanding of how they perceive themselves within DBER and what they perceive it to be. Grounded in the figured worlds theoretical framework, we identify the spectrum of ways emerging STEM education researchers identify or project themselves into this new space: to improve their teaching, to make it their new primary research field, and/or to negotiate how it will fit with their primary one. We also highlight salient negotiations that emerge because of the close ties between DBER and disciplinary science, which provides us with a better understanding of emerging researchers' perceptions. This work generates insight into the kinds of professional development opportunities that would support emerging education researchers within STEM departments and the broader DBER community.

Luis Darmendrail, Alice Gasparini, Andreas Müller

The present text provides a short, non-technical account of some historical and educational background and, based on this, of the rationale of the use of mobile devices in physics education.

Nnaoma M. Oji

A third year medical student shares his approach for translating personal experiences with racial bias into teaching moments that enrich the learning environment.

Christian Bob Nicol, Emmanuel Gakuba, Gonzague Habinshuti

Seventeen years after the end of the Liberian civil war, which is partly blamed for the waning of the standard of education, the country is still grappling with providing a competency-based science educational experience that will enhance the science inquiry process skills of its youth. In this paper we used the constructivist theoretical perspective to compare the science inquiry process skills of Grade 11 students in government and private schools. The study employed a descriptive survey design and the quantitative research method. Six high schools were selected by cluster random sampling, and a total of 360 students constituted the study sample. This study found that government school students have significantly higher perceived science inquiry process skills than their private school counterparts and that an average of 42% of private school students cannot demonstrate any skills related to experimental design, data representation, communication and presentation. Male students indicated having significantly higher science inquiry process skills compared to their female counterparts. However, a varying majority across study groups practise the science inquiry process skills occasionally.

Yufei Xie, Jing Cui, Mengdie Wang

With the development of China's economy and society, the importance of "craftsman's spirit" has become more and more prominent. As the main educational institution for training technical talents, higher vocational colleges vigorously promote the exploration of the cultivation path of craftsman spirit in higher vocational education, which provides new ideas and directions for the reform and development of higher vocational education, and is the fundamental need of the national innovation driven development strategy. Based on the questionnaire survey of vocational students in a certain range, this paper analyzes the problems existing in the cultivation path of craftsman spirit in Higher Vocational Education from multiple levels and the countermeasures.

George Boateng, Samuel John, Andrew Glago et al.

Africa has a high student-to-teacher ratio which limits students' access to teachers. Consequently, students struggle to get answers to their questions. In this work, we extended Kwame, our previous AI teaching assistant, adapted it for science education, and deployed it as a web app. Kwame for Science answers questions of students based on the Integrated Science subject of the West African Senior Secondary Certificate Examination (WASSCE). Kwame for Science is a Sentence-BERT-based question-answering web app that displays 3 paragraphs as answers along with a confidence score in response to science questions. Additionally, it displays the top 5 related past exam questions and their answers in addition to the 3 paragraphs. Our preliminary evaluation of the Kwame for Science with a 2.5-week real-world deployment showed a top 3 accuracy of 87.5% (n=56) with 190 users across 11 countries. Kwame for Science will enable the delivery of scalable, cost-effective, and quality remote education to millions of people across Africa.

Heiko Dietrich, Tanya Evans

Traditional lectures are commonly understood to be a teacher-centered mode of instruction where the main aim is a provision of explanations by an educator to the students. Recent literature in higher education overwhelmingly depicts this mode of instruction as inferior compared to the desired student-centered models based on active learning techniques. First, using a four-quadrant model of educational environments, we address common confusion related to a conflation of two prevalent dichotomies by focusing on two key dimensions: (1) the extent to which students are prompted to engage actively and (2) the extent to which expert explanations are provided. Second, using a case study, we describe an evolution of tertiary mathematics education, showing how traditional instruction can still play a valuable role, provided it is suitably embedded in a student-centered course design. We support our argument by analyzing the teaching practice and learning environment in a third-year abstract algebra course through the lens of Stanislav Dehaene's theoretical framework for effective teaching and learning. The framework, comprising "four pillars of learning", is based on a state-of-the-art conception of how learning can be facilitated according to cognitive science, educational psychology, and neuroscience findings. In the case study, we illustrate how, over time, the unit design and the teaching approach have evolved into a learning environment that aligns with the four pillars of learning. We conclude that traditional lectures can and do evolve to optimize learning environments and that the erection of the dichotomy "traditional instruction versus active learning" is no longer relevant.

Nokwanda P. Mbusi, Kakoma Luneta

Background: Pre-service teachers (PSTs) training does not equip students with adequate skills and knowledge of geometry to enable them to teach this section of mathematics competently. Inadequate teacher knowledge of transformation geometry, in particular, requires intervention that targets PSTs’ faulty reasoning displayed in errors they make. Aim: The aim of this study was to explore the use of Bachelor of Education (BEd) students’ faulty reasoning in geometric translations, in designing a Van Hiele phase-based instructional programme that could address such faulty reasoning. Setting: The setting for the study was a newly established rural university in South Africa. Methods: Tests on geometric translations were administered to BEd Foundation Phase students, followed up by interviews to explore errors made when responding to the test items. The errors were then mapped to the design of a Van Hiele phase-based instructional programme. Results: The results revealed that the students had several misconceptions with geometric translations. The misconceptions were delineated into the errors that the students displayed and these were classified under two themes. The first theme was incorrect properties of transformation and under this theme, the errors were coded as confusing translation with rotation, wrong translation method, incorrect interpretation of coordinates and confusing the x and y axis. The second theme was errors involving basic mathematics operations including wrong diagrammatic representation of coordinates and incorrect calculations. Conclusion: The study showed that if the students’ misconceptions and the resulting errors are mapped to specific instructional approaches, their faulty reasoning in geometric transformations is addressed and effective learning is enhanced.

Steven Van Vaerenbergh, Adrián Pérez-Suay

This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in contemporary digital tools for Mathematics Education (ME). It is aimed at researchers in AI and Machine Learning (ML), for whom we shed some light on the specific technologies that are being used in educational applications; and at researchers in ME, for whom we clarify: i) what the possibilities of the current AI technologies are, ii) what is still out of reach and iii) what is to be expected in the near future. We start our analysis by establishing a high-level taxonomy of AI tools that are found as components in digital ME applications. Then, we describe in detail how these AI tools, and in particular ML, are being used in two key applications, specifically AI-based calculators and intelligent tutoring systems. We finish the chapter with a discussion about student modeling systems and their relationship to artificial general intelligence.

Daniel Adrian, Diann Reischman, Kirk Anderson et al.

Maps are a primary method of displaying statistical data that comes from a geographical frame. Maps are esthetically appealing and make it easier to identify geographic patterns in a dataset. However, few introductory statistical texts and courses explicitly present maps as a way to display data. In this article, we will present examples of different types of statistical maps and illustrate how these maps can be used in the instruction of an introductory statistics course.