Farhan Ali

The aim of this study was to examine how achievement varied within and between schools at different grade levels, and long-term trends in variation within and across multiple countries. We used science achievement data from five cycles of Trends in International Mathematics and Science Study (TIMSS) from 2003 to 2019 involving 10 countries from Asia, Europe, and the United States. Employing exploratory data mining methods of variance decomposition, correlation analysis, and Gaussian mixture modeling of data distributions, we found the following: First, between-school variances generally remained consistent across two decades, suggesting that inequality between schools has not increased over time. Second, between-school variances were relatively small for elementary grade level but increased at secondary grade level, though marginally even for countries with early tracking. Third, higher-achieving schools tended to have more equal student achievement levels than lower-achieving schools, lending within-country support for the “virtuous” efficiency-equality trade-off. We further found that reduced equality within lower-achieving schools was associated with bimodality in achievement distribution. Overall, there is little evidence of inequality across schools changing over time. However, there may be evidence of increased inequalities associated with student subpopulations, particularly within lower-achieving schools, with implications on classroom instruction and school cohesion.

How well adolescents get along with others such as peers and teachers is an important aspect of adolescent development. Current research on adolescent relationship with peers and teachers is limited by classical methods that lack explicit test of predictive performance and cannot efficiently discover complex associations with potential non-linearity and higher-order interactions among a large set of predictors. Here, a transparently reported machine learning approach is utilized to overcome these limitations in concurrently predicting how well adolescents perceive themselves to get along with peers and teachers. The predictors were 99 items from four instruments examining internalizing and externalizing psychopathology, sensation-seeking, peer pressure, and parent-child conflict. The sample consisted of 3232 adolescents (M = 14.0 years, SD = 1.0 year, 49% female). Nonlinear machine learning classifiers predicted with high performance adolescent relationship with peers and teachers unlike classical methods. Using model explainability analyses at the item level, results identified influential predictors related to somatic complaints and attention problems that interacted in nonlinear ways with internalizing behaviors. In many cases, these intrapersonal predictors outcompeted in predictive power many interpersonal predictors. Overall, the results suggest the need to cast a much wider net of variables for understanding and predicting adolescent relationships, and highlight the power of a data-driven machine learning approach with implications on a predictive science of adolescence research.

There 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.

Research in disparate fields of education, psychology and neuroscience suggests that emotions play a central role in learning. We critically examine research at the intersection of emotions, adult learning and neuroscience. First, we review studies in the IJLE related to emotions and adult learning. In particular, we focus on the impact of an IJLE publication that argued using neuroscience research for the important role of emotions in transformative learning theory. We then highlight recent developments in neuroscience of emotions with links to transformative learning, before reflecting on ways to move forward by combining neuroscience research with transformative learning theory.

Self-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.

Background 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.

Advances 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.