Search Results (8)

General large language models (LLMs) often suffer from semantic misinterpretation, information redundancy, and hallucinated content when applied to educational question-answering tasks. These issues hinder their effectiveness in supporting students’ specialized course learning and self-directed study. To address these challenges, this study proposes an intelligent tutoring model that integrates a knowledge graph with a large language model (KG-CQ). Focusing on the Data Structures (C Language) course, the model constructs a course-specific knowledge graph stored in a Neo4j graph database. It incorporates modules for knowledge retrieval, domain-specific question answering, and knowledge extraction, forming a closed-loop system designed to enhance semantic comprehension and domain adaptability. A total of 30 students majoring in Educational Technology at H University were randomly assigned to either an experimental group or a control group, with 15 students in each. The experimental group utilized the KG-CQ model during the answering process, while the control group relied on traditional learning methods. A total of 1515 data points were collected. Experimental results show that the KG-CQ model performs well in both answer accuracy and domain relevance, accompanied by high levels of student satisfaction. The model effectively promotes self-directed learning and provides a valuable reference for the development of knowledge-enhanced question-answering systems in educational settings. Full article

This research aims to examine the effect of artificial intelligence (AI)-supported sustainable geography education on the preparation process for the International Geography Olympiad (IGEO). Research was designed according to the simultaneous triangulation design, which is one of the mixed-methods designs. The research is a quasi-experimental model in terms of revealing the effects of independent variables (IGEO) on dependent variables (artificial). In this study, a quasi-experimental design with a pre-test–post-test control group was used. In this mixed-method study, quantitative data were obtained from questionnaires and achievement tests, while qualitative data were obtained from semi-structured interviews with students and teachers. The quantitative data collection tools used in the study were a mapping literacy achievement test and a problem-solving skills perception scale. The data were obtained from students across various class sections of the same school. Qualitative data were collected through semi-structured individual interview forms, observation forms, participant diaries, and focus group interview forms. Hierarchical regression analysis and ANOVA were used to analyze the statistical data, and the inductive analysis technique was used to analyze the qualitative data. The findings show that AI-supported sustainable geography education improves spatial thinking skills, individualized learning, and learning motivation. In the IGEO exam, students answered the field questions. Full article

The purpose of this research is to evaluate the feasibility, acceptability, and preliminary efficacy of a household food-waste prevention and minimization intervention, titled the Culinary Home Empowerment for Food Waste Prevention and Minimization (CHEF-WPM), which consists of a culinary education video series for home cooks. The specific aims are to (1) assess the effects of the intervention at a population level across process (feasibility, usage, acceptability, satisfaction) and preliminary efficacy (motivation, opportunity, ability) metrics and (2) assess the effects of the intervention at a community level across process (feasibility, usage, acceptability, satisfaction) and preliminary efficacy (motivation, opportunity, ability, household food waste, sustainable dietary practices) metrics. The intervention includes eight modules, each containing three to five brief videos, as well as downloadable recipes and worksheets. The evaluation will explore the effects of the program through two distinct investigations, namely (1) voluntary access to the intervention content in a population-based setting and (2) intensive delivery of the intervention content as part of a remote class in a community setting. Evaluation of the intervention in the population-based setting will use a single-arm, quasi-experimental post-test only study design. All home cooks who access the videos will be invited to answer a five-question post-video survey about acceptability, satisfaction, and potential implementation of the learning. A separate sample of individuals will be recruited to participate in a more in-depth evaluation (pre- and multiple post-test survey). Evaluation of the community-based intervention will use a mixed methods study design. Findings from the two distinct evaluation studies will be jointly discussed and triangulated to support larger conclusions about the intervention’s desirability, impact on motivation, opportunity, ability, and food waste, and the potential directions for further improvement. Full article

A 63-year-old symptomatic female with apical hypertrophic cardiomyopathy and diastolic disfunction was admitted to the hospital. What is the best way to manage this patient? This study is a literature review that was performed to answer this question. The following PubMed search strategy was used: ‘Hypertrophic obstructive cardiomyopathy’ [All Fields] OR ‘apical myectomy’ [All Fields], NOT ‘animal [mh]’ NOT ‘human [mh]’ NOT ‘comment [All Fields]’ OR ‘editorial [All Fields]’ OR ‘meta-analysis [All Fields]’ OR ‘practice-guideline [All Fields]’ OR ‘review [All Fields]’ OR ‘pediatrics [mh]’. The natural history of the disease has a benign prognosis; however, a watchful strategy was associated with the risk of adverse cardiovacular events. Contrastingly, transapical myectomy was associated with low surgical risk and acceptable outcomes. In our case, the patient underwent transapical myectomy with an unconventional post-operative period. Control echocardiography showed marked left ventricular (LV) cavity enlargement: LV end-diastolic volume, 74 mL; LV ejection fraction, 65%; and LV stroke volume index increased to 27 mL/m². The patient was discharged 7 days after myectomy. At 6 months post-operation, the patient was NYHA Class I, with a 6 min walk test score of 420 m. Therefore, transapical myectomy may be considered as a feasible procedure in patients with apical hypertrophic cardiomyopathy and progressive heart failure. Full article

To what extent does the use of Voting Advice Applications in (digital) civic education classes at school impact students’ political knowledge, attitudes, motivations and behavioral dispositions toward elections? This article provides answers to this question by presenting a sample analysis of the responses of 1189 pupils who participated in a digital civic education intervention, with the German Voting Advice Application Wahl-O-Mat at its core, whose usage was embedded in an elaborated didactical concept in civic education classes. Using a quasi-experimental field design with pre- and post-tests, the study shows that the intervention substantially improves students’ knowledge of the investigated state election. Furthermore, we can trace a significant increase in young people’s political efficacy and specific interest in the election campaign. Finally, we observe a substantial increase in intended electoral participation, especially among those adolescents whose intention to participate in elections was low prior to the intervention, which contributes to a reduction in existing participation gaps. In particular, we identify changes in motivational and cognitive political dispositions, but only to a limited extent evaluative and sociodemographic background variables, as key factors driving the intervention-induced change in willingness to participate in the state election. Our paper concludes by discussing the limitations of the study and its implications for empirical research and practice in civic education. Full article

Whereas traditional teaching environments encourage lively and engaged interaction and reward extrovert qualities, introverts, and others with symptoms that make social engagement difficult, such as autism spectrum disorder (ASD), are often disadvantaged. This population is often more engaged in quieter, low-key learning environments and often does not speak up and answer questions in traditional lecture-style classes. These individuals are often passed over in school and later in their careers for not speaking up and are assumed to not be as competent as their gregarious and outgoing colleagues. With the rise of the metaverse and democratization of virtual reality (VR) technology, post-secondary education is especially poised to capitalize on the immersive learning environments social VR provides and prepare students for the future of work, where virtual collaboration will be key. This study seeks to reconsider the role of VR and the metaverse for introverts and those with ASD. The metaverse has the potential to continue the social and workplace changes already accelerated by the pandemic and open new avenues for communication and collaboration for a more inclusive audience and tomorrow. Full article

The discussion about the use of digital technologies in education is not new. However, the COVID-19 pandemic and the total closure of schools around the world, that forced millions of students to attend their classes from home, has demonstrated the importance of this discussion. It has highlighted the need to revisit debates about the interactions between technology and education, and the added value of digital resources to enhance the educational process. This article, based on an exploratory analysis, aims to understand how the transition from face-to-face to digital was accomplished in Portuguese primary and secondary education, namely regarding teacher training and the difficulties experienced during the emergency remote education period. The data analysed in this article were collected through an online questionnaire, disseminated through online social networks, and answered by 136 Portuguese primary and secondary education teachers. The questions focused on this article were open-ended, and the information collected was analysed using content analysis methodology. The results show how teachers have been forced to modify their pedagogical work, the importance of training, and the inherent challenges and critical reflections associated with the process, as well as the opportunities presented in a post-pandemic educational reality. Full article

Social media sites are considered one of the most important sources of data in many fields, such as health, education, and politics. While surveys provide explicit answers to specific questions, posts in social media have the same answers implicitly occurring in the text. This research aims to develop a method for extracting implicit answers from large tweet collections, and to demonstrate this method for an important concern: the problem of heart attacks. The approach is to collect tweets containing “heart attack” and then select from those the ones with useful information. Informational tweets are those which express real heart attack issues, e.g., “Yesterday morning, my grandfather had a heart attack while he was walking around the garden.” On the other hand, there are non-informational tweets such as “Dropped my iPhone for the first time and almost had a heart attack.” The starting point was to manually classify around 7000 tweets as either informational (11%) or non-informational (89%), thus yielding a labeled dataset to use in devising a machine learning classifier that can be applied to our large collection of over 20 million tweets. Tweets were cleaned and converted to a vector representation, suitable to be fed into different machine-learning algorithms: Deep neural networks, support vector machine (SVM), J48 decision tree and naïve Bayes. Our experimentation aimed to find the best algorithm to use to build a high-quality classifier. This involved splitting the labeled dataset, with 2/3 used to train the classifier and 1/3 used for evaluation besides cross-validation methods. The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). In addition, it obtained the highest F1-scores with (73.6%) and (97.4%) for informational and non-informational classes, respectively. Full article