Options
Farhan Ali
Preferred name
Farhan Ali
Email
farhan.ali@nie.edu.sg
Department
Learning Sciences and Assessment (LSA)
ORCID
14 results
Now showing 1 - 10 of 14
- PublicationOpen AccessLearning data science in elementary school mathematics: A comparative curriculum analysisBackground Data literacy is increasingly important in today’s data-driven world. Students across many educational systems first formally learn about data in elementary school not as a separate subject but via the mathematics curriculum. This experience can create tensions in the priorities of learning and assessment given the presence of other foundational mathematics content domains such as numbers, algebra, measurement, and geometry. There is a need to study data literacy in comparison to these other content domains in elementary mathematics. To address this need, we developed a methodology motivated by thinking curriculum theory and aligned with international assessment framework, for comparative analysis across mathematics content domains. This methodology examined increasing levels of cognitive domains from knowing to applying to reasoning across mathematics content domains. Intended, assessed, and attained curricula were analyzed using Singapore as a case study, combined with broader comparisons to attainments in four East Asian countries in TIMSS, an international large-scale assessment. Results We found that learning in the data domain had very limited coverage in intended and assessed curricula in Singapore. However, compared to other mathematics content domains, the data curriculum placed heavier emphasis on higher-order cognitive domains including the use of generally difficult mixed data visualizations. This demanding curriculum in Singapore was associated with the highest attainment in the data domain among average 4th grade Singaporean students relative to students in four East Asian countries in TIMSS, as analyzed by quantile regression. However, lower-performing Singaporean students at the 10th percentile generally did not outperform their East Asian peers. We further found very limited applications of data in other mathematics domains or cross-domain learning more generally. Conclusion Our study offers a comparative analysis of the data curriculum in elementary school mathematics education. While the data curriculum was cognitively demanding and translated to very high average attainments of Singaporean students, the curriculum did not equally help weaker Singaporean students, with implications on current discourse on equity–excellence trade-off in science, technology, engineering, and mathematics (STEM) education. Our study further highlights the lack of cross-domain learning in mathematics involving data. Despite the broad applicability of data science, elementary school students’ first formal experience with data may lack emphasis on its cross-domain applications, suggesting a need to further integrate data skills and competencies into the mathematics curriculum and beyond.
WOS© Citations 2Scopus© Citations 8 126 210 - PublicationOpen AccessNMASTE: Network meta-analysis in translating educational neuroscience(National Institute of Education, Nanyang Technological University (NIE NTU), Singapore, 2024)
; ; 74 439 - PublicationMetadata onlyAutomatic item generation in various STEM subjects using large language model prompting(Elsevier, 2025)
; ; 25 - PublicationEmbargoDistinct social factors are linked to epistemic curiosity and digital information‐seeking among adolescents: Generalizability across 41 countries
Introduction Curiosity, the intrinsic motivation to sense, know, and experience the unknown, plays important roles in adolescent achievement and well-being. Theoretical considerations and empirical research suggest the contribution of social relationships in fostering curiosity. However, curiosity is expressed in different forms and contexts. Here, we investigated the social predictors of general epistemic curiosity, and of different forms of digital information-seeking in adolescents.
Methods
Nationally representative cross-sectional data from Programme for International Student Assessment 2022 were used (N = 327,778 from 41 countries, 15.8 years, 49.6% female). Multiple regression was implemented using four different types of social relationships—teacher relationship, school belonging, bullying victimization, family relationship—as predictors of three forms of curiosity and information-seeking—general epistemic curiosity, and digital information-seeking for formal learning and for informal learning purposes.Results and Conclusion
Teacher–student relationship was linked to general epistemic curiosity but less so, or not at all, to digital information-seeking. Instead, family relationship and bullying victimization were more important drivers of digital information-seeking for formal and informal learning purposes respectively. These distinctions were largely generalizable across 41 countries examined. The findings paint a complex picture of how figures in different adolescent social spheres matter for different forms of epistemic curiosity and information-seeking, with practical and theoretical implications.21 46 - PublicationMetadata onlySupporting self-directed learning and self-assessment using TeacherGAIA, a generative AI chatbot application: Learning approaches and prompt engineeringSelf-directed learning and self-assessment require student responsibility over learning needs, goals, processes, and outcomes. However, this student-led learning can be challenging to achieve in a classroom limited by a one-to-many teacher-led instruction. We, thus, have designed and prototyped a generative artificial intelligence chatbot application (GAIA), named TeacherGAIA, that can be used to asynchronously support students in their self-directed learning and self-assessment outside the classroom. We first identified diverse constructivist learning approaches that align with, and promote, student-led learning. These included knowledge construction, inquiry-based learning, self-assessment, and peer teaching. The in-context learning abilities of large language model (LLM) from OpenAI were then leveraged via prompt engineering to steer interactions supporting these different learning approaches. These interactions contrasted with ChatGPT, OpenAI’s chatbot which by default engaged in the traditional transmissionist mode of learning reminiscent of teacher-led instruction. Preliminary design, prompt engineering and prototyping suggested fidelity to the learning approaches, cognitive guidance, and social-emotional support, all of which were implemented in a generative AI manner without pre-specified rules or “hard-coding”. Other affordances of TeacherGAIA are discussed and future development outlined. We anticipate TeacherGAIA to be a useful application for teachers in facilitating self-directed learning and self-assessment among K-12 students.
Scopus© Citations 10 294 - PublicationOpen AccessFuture of learning: Understanding and emphasising the future learnersThere is much interest in envisioning how future of learning will be. This discourse, what some have called futurizing, has emphasised various aspects of 21st century competencies, emerging technologies, and innovative pedagogies with focus on key stakeholders such as teachers, schools, and governments. While these aspects of futurising about learning are important, my presentation argued for the understanding of learners, how they likely will navigate learning, what their aspirations and limitations are, and what and how they want the future to be. These considerations are important as they impact future technological designs, highlight potential tensions, and ground the possibilities of what kind of futures are realizable and what are fictional.
66 131 - PublicationMetadata onlyNegative self-concept: Cross-country evidence of its importance for understanding motivation and academic achievementAcademic self-concept, the belief in one’s ability, is a key motivational construct in educational psychology and large-scale assessments. The construct is typically measured by instruments with positively (“I usually do well in science”) and negatively worded items (“I am just not good in science”). A single latent factor is often assumed. Here, we investigated this assumption using international large-scale assessment data across two age groups of children in fourth grade and adolescents in eighth grade (N = 296,320 students, 23 educational systems). We, instead, found strong evidence of the substantiveness of a negative self-concept factor derived from negatively worded items. Exploratory and confirmatory factor analyses uncovered negative self-concept as being distinct from positive self-concept. Furthermore, theory-driven modeling supported the internal/external (I/E) frame of reference model effect on negative self-concept: achievement has a stronger effect on eighth graders’ negative self-concept relative to fourth-grade children across many countries, especially for mathematics. Overall, understanding students’ negative appraisals and negative beliefs of their ability is an important theoretical and policy imperative.
43 - PublicationOpen AccessComprehensive analysis of feature extraction methods for emotion recognition from multichannel EEG recordingsAdvances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies in this area have used relatively small monocentric data and focused on a limited range of EEG features, making it difficult to compare the utility of different sets of EEG features for emotion recognition. This study addressed that by comparing the classification accuracy (performance) of a comprehensive range of EEG feature sets for identifying emotional states, in terms of valence and arousal. The classification accuracy of five EEG feature sets were investigated, including statistical features, fractal dimension (FD), Hjorth parameters, higher order spectra (HOS), and those derived using wavelet analysis. Performance was evaluated using two classifier methods, support vector machine (SVM) and classification and regression tree (CART), across five independent and publicly available datasets linking EEG to emotional states: MAHNOB-HCI, DEAP, SEED, AMIGOS, and DREAMER. The FD-CART feature-classification method attained the best mean classification accuracy for valence (85.06%) and arousal (84.55%) across the five datasets. The stability of these findings across the five different datasets also indicate that FD features derived from EEG data are reliable for emotion recognition. The results may lead to the possible development of an online feature extraction framework, thereby enabling the development of an EEG-based emotion recognition system in real time.
WOS© Citations 5Scopus© Citations 26 100 185 - PublicationMetadata onlySubjective well‐being of children with special educational needs: Longitudinal predictors using machine learningChildren with special educational needs (SEN) are a diverse group facing numerous challenges related to well-being and mental health. Understanding the predictors of well-being in this population requires the incorporation of diverse factors along with approaches that can uncover complexity in how these factors work together to influence well-being. We longitudinally predicted subjective well-being in a group of children with diverse special educational needs (N = 499; M = 8.4 ± 0.9 years). Thirty-two variables - ranging from demographics to various categories of life experiences - were used as predictors for both nonlinear machine learning and classical linear classifiers. Nonlinear machine learning classifiers exhibited much performance in predicting subjective well-being (F1 score = 0.72 to 0.84) compared to traditional linear classifiers. Overall, across all children, prior subjective well-being, numeracy, literacy skills, and interpersonal dimensions played important roles. However, clustering further identified four distinct clusters sharing important predictors: a ‘socializer’ cluster dominated by interpersonal functioning predictors, an ‘analyzer’ cluster emphasizing academic skills predictors, and two clusters with more diverse sets of important predictors. Our research highlights the multiple pathways toward well-being in children with SEN as uncovered by machine learning, with implications for understanding and supporting their well-being.
53 - PublicationMetadata onlyTechnologies, neuroscience and education
Aided by rapid developments in technologies, there has been an explosion of knowledge about the brain in the past two decades. This neuroscientific knowledge is increasingly penetrating the field of education. This chapter covers topics at the intersection of technologies, neuroscience and education. First, a brief overview is given of the technologies that have enabled discoveries about the brain relevant for learning. Second, the chapter elaborates on how a better understanding of the brain has made pertinent impacts for practice and policy. Finally, the chapter discusses emerging technologies for the future of education. While most work has been done overseas, relevance to the local classroom contexts will be made appropriately.
Neuroscience and education have traditionally been separate disciplines with different, sometimes conflicting, underlying philosophies and approaches. However, intersections between the two are increasingly being developed. Leslie Hart, a psychologist, argues that designing educational experiences without an understanding of the brain is like designing a glove without an understanding of the human hand. This chapter addresses this important need to optimise learning experiences using neuroscience knowledge.
91