Hasil untuk "Special aspects of education"

Fiammetta Caccavale, Carina L. Gargalo, Julian Kager et al.

The landscape of education is changing rapidly, shaped by emerging pedagogical approaches, technological innovations such as artificial intelligence (AI), and evolving societal expectations, all of which demand thorough evaluation of new educational tools. Although large language models (LLMs) present substantial opportunities especially in Higher Education, their propensity to generate hallucinations and their limited specialized knowledge may introduce significant risks. This study aims to address these risks by examining the practical implementation of an LLM-enhanced assistant in a university level course. We implemented a generative AI assistant grounded in a retrieval-augmented generation (RAG) model to replicate a previously teacher-led, time-intensive exercise. To assess the effectiveness of the LLM, we conducted three separate experiments through iterative mixed-methods approaches, including a crossover design. The resulting data address central research questions related to student motivation, perceived differences between engaging with the LLM versus a human teacher, the quality of AI-generated responses, and the impact of the LLM on students' academic performance. The results offer direct insights into students' views and the pedagogical feasibility of embedding LLMs into specialized courses. Finally, we discuss the main challenges, opportunities and future directions of LLMs in teaching and learning in Higher Education.

Russell Beale

Generative AI tools - most notably large language models (LLMs) like ChatGPT and Codex - are rapidly revolutionizing computer science education. These tools can generate, debug, and explain code, thereby transforming the landscape of programming instruction. This paper examines the profound opportunities that AI offers for enhancing computer science education in general, from coding assistance to fostering innovative pedagogical practices and streamlining assessments. At the same time, it highlights challenges including academic integrity concerns, the risk of over-reliance on AI, and difficulties in verifying originality. We discuss what computer science educators should teach in the AI era, how to best integrate these technologies into curricula, and the best practices for assessing student learning in an environment where AI can generate code, prototypes and user feedback. Finally, we propose a set of policy recommendations designed to harness the potential of generative AI while preserving the integrity and rigour of computer science education. Empirical data and emerging studies are used throughout to support our arguments.

Cynthia Zastudil, David H. Smith, Yusef Tohamy et al.

Ensuring equitable access to computing education for all students-including those with autism, dyslexia, or ADHD-is essential to developing a diverse and inclusive workforce. To understand the state of disability research in computing education, we conducted a systematic literature review of research on neurodiversity in computing education. Our search resulted in 1,943 total papers, which we filtered to 14 papers based on our inclusion criteria. Our mixed-methods approach analyzed research methods, participants, contribution types, and findings. The three main contribution types included empirical contributions based on user studies (57.1%), opinion contributions and position papers (50%), and survey contributions (21.4%). Interviews were the most common methodology (75% of empirical contributions). There were often inconsistencies in how research methods were described (e.g., number of participants and interview and survey materials). Our work shows that research on neurodivergence in computing education is still very preliminary. Most papers provided curricular recommendations that lacked empirical evidence to support those recommendations. Three areas of future work include investigating the impacts of active learning, increasing awareness and knowledge about neurodiverse students' experiences, and engaging neurodivergent students in the design of pedagogical materials and computing education research.

Hang Li, Tianlong Xu, Chaoli Zhang et al.

The recent surge in generative AI technologies, such as large language models and diffusion models, has boosted the development of AI applications in various domains, including science, finance, and education. Concurrently, adaptive learning, a concept that has gained substantial interest in the educational sphere, has proven its efficacy in enhancing students' learning efficiency. In this position paper, we aim to shed light on the intersectional studies of these two methods, which combine generative AI with adaptive learning concepts. By presenting discussions about the benefits, challenges, and potentials in this field, we argue that this union will contribute significantly to the development of the next-stage learning format in education.

Hambur Wang

This study explores the impact of appearance discrimination in the labor market and whether education can mitigate this issue. A statistical analysis of approximately 1.058 million job advertisements in China from 2008 to 2010 found that about 7.7% and 2.6% of companies had explicit requirements regarding candidates' appearance and height, particularly in positions with lower educational requirements. Literature review indicates that attractive job seekers typically enjoy higher employment opportunities and wages, while unattractive individuals face significant income penalties. Regression analysis of 1,260 participants reveals a significant positive correlation between attractiveness scores and wages, especially in low-education groups. Conversely, in high-education groups, the influence of appearance on income is not significant. The study suggests that enhancing education levels can effectively alleviate income declines associated with appearance, providing policy recommendations to reduce appearance discrimination in the labor market.

Zhipeng Yin, Sribala Vidyadhari Chinta, Zichong Wang et al.

The integration of AI in education holds immense potential for personalizing learning experiences and transforming instructional practices. However, AI systems can inadvertently encode and amplify biases present in educational data, leading to unfair or discriminatory outcomes. As researchers have sought to understand and mitigate these biases, a growing body of work has emerged examining fairness in educational AI. These studies, though expanding rapidly, remain fragmented due to differing assumptions, methodologies, and application contexts. Moreover, existing surveys either focus on algorithmic fairness without an educational setting or emphasize educational methods while overlooking fairness. To this end, this survey provides a comprehensive systematic review of algorithmic fairness within educational AI, explicitly bridging the gap between technical fairness research and educational applications. We integrate multiple dimensions, including bias sources, fairness definitions, mitigation strategies, evaluation resources, and ethical considerations, into a harmonized, education-centered framework. In addition, we explicitly examine practical challenges such as censored or partially observed learning outcomes and the persistent difficulty in quantifying and managing the trade-off between fairness and predictive utility, enhancing the applicability of fairness frameworks to real-world educational AI systems. Finally, we outline an emerging pathway toward fair AI-driven education and by situating these technologies and practical insights within broader educational and ethical contexts, this review establishes a comprehensive foundation for advancing fairness, accountability, and inclusivity in the field of AI education.

Kristjan-Julius Laak, Jaan Aru

Personalized learning (PL) aspires to provide an alternative to the one-size-fits-all approach in education. Technology-based PL solutions have shown notable effectiveness in enhancing learning performance. However, their alignment with the broader goals of modern education is inconsistent across technologies and research areas. In this paper, we examine the characteristics of AI-driven PL solutions in light of the goals outlined in the OECD Learning Compass 2030. Our analysis indicates a gap between the objectives of modern education and the technological approach to PL. We identify areas where the AI-based PL solutions could embrace essential elements of contemporary education, such as fostering learner's agency, cognitive engagement, and general competencies. While the PL solutions that narrowly focus on domain-specific knowledge acquisition are instrumental in aiding learning processes, the PL envisioned by educational experts extends beyond simple technological tools and requires a holistic change in the educational system. Finally, we explore the potential of generative AI, such as ChatGPT, and propose a hybrid model that blends artificial intelligence with a collaborative, teacher-facilitated approach to personalized learning.

Yong Zheng

ChatGPT, an implementation and application of large language models, has gained significant popularity since its initial release. Researchers have been exploring ways to harness the practical benefits of ChatGPT in real-world scenarios. Educational researchers have investigated its potential in various subjects, e.g., programming, mathematics, finance, clinical decision support, etc. However, there has been limited attention given to its application in data science education. This paper aims to bridge that gap by utilizing ChatGPT in a data science course, gathering perspectives from students, and presenting our experiences and feedback on using ChatGPT for teaching and learning in data science education. The findings not only distinguish data science education from other disciplines but also uncover new opportunities and challenges associated with incorporating ChatGPT into the data science curriculum.

Anastasia Olga, Tzirides, Akash Saini et al.

The launch of ChatGPT in November 2022 precipitated a panic among some educators while prompting qualified enthusiasm from others. Under the umbrella term Generative AI, ChatGPT is an example of a range of technologies for the delivery of computer-generated text, image, and other digitized media. This paper examines the implications for education of one generative AI technology, chatbots responding from large language models, or C-LLM. It reports on an application of a C-LLM to AI review and assessment of complex student work. In a concluding discussion, the paper explores the intrinsic limits of generative AI, bound as it is to language corpora and their textual representation through binary notation. Within these limits, we suggest the range of emerging and potential applications of Generative AI in education.

William K. Black, Rebecca L. Matz, Mark Mills et al.

Problem Roulette (PR), an online study service at the University of Michigan, offers points-free formative practice to students preparing for examinations in introductory STEM courses. Using four years of PR data involving millions of problem attempts by thousands of students, we quantify benefits of increased practice study volume in introductory physics. After conditioning mean final grade on standardized (ACT/SAT) math test score, we analyze deviations based on student study volume. We find a strong effect; mean course grade rises quadratically with the logarithm of the total number of PR questions encountered over the term ($N_{\rm Q,tot}$), with an overall gain of $0.77 \pm 0.12$ grade points between $1 < N_{\rm Q,tot} < 1000$. The gains are persistent across the range of math test score represented in our sample. While $N_{\rm Q,tot}$ surely correlates with other study habits, the benefits of increased study in general still hold. A model for final grade using test score and study volume largely accounts for demographic stratification, including by sex, parental education level, number of parents at home, nationality / underrepresented minority status, and regional income level, with two significant exceptions: students whose parents did not earn a college degree, who earn $-0.27 \pm 0.04$ grade points ($6.1σ$) below expectations and underrepresented minority students at $-0.14 \pm 0.04$ points ($3.6σ$). Residual scatter in final grade remains comparable to the maximal study gains, implying that the model is far from deterministic: individual variation trumps mean trends. Our findings can help motivate students to study more and help teachers to identify which types of students may especially need such encouragement.

Thierry Noah Dana-Picard

This survey paper is an expanded version of an invited keynote at the ThEdu'22 workshop, August 2022, in Haifa (Israel). After a short introduction on the developments of CAS, DGS and other useful technologies, we show implications in Mathematics Education, and in the broader frame of STEAM Education. In particular, we discuss the transformation of Mathematics Education into exploration-discovery-conjecture-proof scheme, avoiding usage as a black box . This scheme fits well into the so-called 4 C's of 21st Century Education. Communication and Collaboration are emphasized not only between humans, but also between machines, and between man and machine. Specific characteristics of the outputs enhance the need of Critical Thinking. The usage of automated commands for exploration and discovery is discussed, with mention of limitations where they exist. We illustrate the topic with examples from parametric integrals (describing a "cognitive neighborhood" of a mathematical notion), plane geometry, and the study of plane curves (envelopes, isoptic curves). Some of the examples are fully worked out, others are explained and references are given.

Sruti Mallik, Ahana Gangopadhyay

Quality education, one of the seventeen sustainable development goals (SDGs) identified by the United Nations General Assembly, stands to benefit enormously from the adoption of artificial intelligence (AI) driven tools and technologies. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled massive research and development efforts in the artificial intelligence for education (AIEd) sector. In this review article, we investigate how artificial intelligence, machine learning and deep learning methods are being utilized to support students, educators and administrative staff. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety - from students admissions, course scheduling etc. in the proactive planning phase to knowledge delivery, performance assessment etc. in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 194 original research articles published in the past two decades i.e., 2003 - 2022. We discuss the paradigm shifts in the solution approaches proposed, i.e., in the choice of data and algorithms used over this time. We further dive into how the COVID-19 pandemic challenged and reshaped the education landscape at the fag end of this time period. Finally, we pinpoint existing limitations in adopting artificial intelligence for education and reflect on the path forward.

Atakan Coban, Niyazi Coban

Considering the 21st century skills and the importance of STEM education in fulfilling these skills, it is clear that the course materials should be materials that bring students together with technology and attract their attention, apart from traditional materials. In addition, in terms of the applicability of these materials, it is very important that the materials are affordable and easily accessible. In this study two open ended resonance tube, Computer and speaker for generate sound with different frequencies, Arduino UNO, AR-054 Sound Sensor, Green LED and 220 ohm resistance were used for measure the speed of sound in air at room tempature. With the help of sound sensor, two consecutive harmonic frequency values were determined and the fundamental frequency was calculated. Using the tube features and the fundamental frequency value, the speed of sound propagation in the air at room temperature was calculated as 386.42 m/s. This value is theoretically 346 m/s. This study, in which the propagation speed of the sound is calculated with very low cost and coding studies with 12% error margin, is important in terms of hosting all STEM gains and can be easily applied in classrooms.

Enfa George, Mihai Surdeanu

We introduce SexTok, a multi-modal dataset composed of TikTok videos labeled as sexually suggestive (from the annotator's point of view), sex-educational content, or neither. Such a dataset is necessary to address the challenge of distinguishing between sexually suggestive content and virtual sex education videos on TikTok. Children's exposure to sexually suggestive videos has been shown to have adversarial effects on their development. Meanwhile, virtual sex education, especially on subjects that are more relevant to the LGBTQIA+ community, is very valuable. The platform's current system removes or penalizes some of both types of videos, even though they serve different purposes. Our dataset contains video URLs, and it is also audio transcribed. To validate its importance, we explore two transformer-based models for classifying the videos. Our preliminary results suggest that the task of distinguishing between these types of videos is learnable but challenging. These experiments suggest that this dataset is meaningful and invites further study on the subject.

Benard Mulilo, Mounia Laassiri, Diallo Boye

Higher education and advanced scientific research lead to social, economic, and political development of any country. All developed societies like the current 2022 G7 countries: Canada, France, Germany, Italy, Japan, the UK, and the US have all not only heavily invested in higher education but also in advanced scientific research in their respective countries. Similarly, for African countries to develop socially, economically, and politically, they must follow suit by massively investing in higher education and local scientific research.

Joël Terschuur

Educational attainment generates labor market returns, societal gains and has intrinsic value for individuals. We study Inequality of Opportunity (IOp) and intergenerational mobility in the distribution of educational attainment. We propose to use debiased IOp estimators based on the Gini coefficient and the Mean Logarithmic Deviation (MLD) which are robust to machine learning biases. We also measure the effect of each circumstance on IOp, we provide tests to compare IOp in two populations and to test joint significance of a group of circumstances. We find that circumstances explain between 38\% and 74\% of total educational inequality in European countries. Mother's education is the most important circumstance in most countries. There is high intergenerational persistence and there is evidence of an educational Great Gatsby curve. We also construct IOp aware educational Great Gatsby curves and find that high income IOp countries are also high educational IOp and less mobile countries.

Raphael Watschinger, Michal Merta, Günther Of et al.

We present a novel approach to the parallelization of the parabolic fast multipole method for a space-time boundary element method for the heat equation. We exploit the special temporal structure of the involved operators to provide an efficient distributed parallelization with respect to time and with a one-directional communication pattern. On top, we apply a task-based shared memory parallelization and SIMD vectorization. In the numerical tests we observe high efficiencies of our parallelization approach.

Leon Andretti Abdillah, Arif Ainur Rofiq, Dian Eka Indriani

The awareness of an environmentally friendly learning process has been of concern lately. IT offers some applications that are able to provide better green education environment in higher education sectors. This research involves top social information technology applications, DropBox and WordPress. DropBox is the most popular cloud storage in the world. Meanwhile, WordPress is top content management systems at the moment. This research employs the utilization of those two applications in students assignments and presentation medium. The study observed 150 sophomore students in computer science faculty. The results show that, involving DropBox and WordPress in higher education learning activities able to significantly reduce paper and ink usage. The combination of those two top applications create a very handy and comfortable environment for students in higer education. This strategy reduce the consumption of papers and inks and are accepted well by most of the students.

Wendy Wang, Yu Tao, Kai Wang et al.

As the Internet grows in importance, it is vital to develop methods and techniques for educating end-users to improve their awareness of online privacy. Web-based education tools have been proven effective in many domains and have been increasingly adopted by many online professional and educational services. However, the design and development of Web-based education tools for online privacy is still in the early stage. The traditional solutions always involve privacy experts who have sophisticated expertise. Such involvement can make the tool development costly. Furthermore, it is not clear how inspiring and effective these education tools are to general users of varying backgrounds, specially to novice users who have rarely dealt with online privacy issues before. In this paper, we design, develop, and evaluate a game-based privacy learning system by leveraging the wisdom of a crowd of non-experts on Amazon Mechanic Turk. Empirical study demonstrates that the crowd can provide high-quality ideas of designing and developing a practical, educational privacy learning game.

Wei Liu, Wei-Lu Wang, C. S. Liu et al.

Proposed as blanket structural materials for fusion power reactors, reduced activation ferritic/martensitic (RAFM) steel undergoes volume expanding and contracting in a cyclic mode under service environment. Particularly, being subjected to significant fluxes of fusion neutrons RAFM steel suffers considerable local volume variations in the radiation damage involved regions. It is necessary to study the structure properties of the alloying elements in contraction and expansion states. In this paper we studied local substitution structures of thirteen alloying elements Al, Co, Cr, Cu, Mn, Mo, Nb, Ni, Si, Ta, Ti, V, and W in bcc Fe and calculated their substitutional energies in the volume variation range from -1.0% to 1.0%. From the structure relaxation results of the first five neighbor shells around the substitutional atom we find the relaxation in each neighbor shell keeps approximately uniform within the volume variation from -1.0% to 1.0% except those of Mn and the relaxation of the fifth neighbor shell is stronger than that of the third and forth, indicating that the lattice distortion due to the substitution atom is easier to spread in <111> direction than in other direction. The relaxation pattern and intensity are related to the size and electron structure of the substitutional atom. For some alloying elements, such as Mo, Nb, Ni, Ta, Ti and W, the substitutional energy decreases noticeably when the volume increases. Further analysis show that the substitutional energy comprises the energy variation originated from local structure relaxation and the chemical potential difference of the substitutional atom between its elemental crystalline state and the solid solution phase in bcc Fe. We think the approximately uniform relaxation of each neighbor shell around a substitutional atom give rise to a linear decrease in the substitutional energy with the increasing volume.