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Quality-Adaptive Multimodal Face Recognition with Streaming Prototype Learning and CrossModal Evaluation
(SDU University, 2026) Anarbayeva D.
While state-of-the-art face recognition systems achieve near-perfect accuracy under ideal conditions, their performance degrades significantly in real-world conditions when the input images are blurred, noisy, have partially-occluded faces, or are of low resolution. Multi-modal face recognition systems that combine visible-spectrum, near-infrared (NIR), and depth sensor inputs tend to exhibit even greater performance degradation under these conditions because each sensor modality suffers degradation in different ways. In this dissertation, we consider three main problems with current multi-modal face recognition systems: estimating the quality of the input images from each sensor modality, weighting the inputs according to their estimated quality, and maintaining the identity galleries within the recognition systems in an efficient manner. A new quality estimation function Q(I)is proposed for each input image I that utilises noisecorrected sharpness, noise level, face coverage, and contrast to provide a score that represents the quality of the image without requiring training data. When evaluated on the LFW face dataset with synthetic image degradations, the quality estimates have a Spearman rank correlation coefficient ρ of 0.90 or higher for all four types of image degradation, while the baseline method based on the variance of the Laplacian kernel has a negative correlation with certain types of degradation. When incorporating the quality score estimates into the fusion of visible-spectrum, NIR, and depth sensor recognition results, the system achieves up to a 4.4 percentage point improvement in Rank-1 recognition accuracy under degraded NIR inputs. A streaming framework for updating the identity galleries that utilises an exponential moving average with a momentum value of 0.9 and reservoir sampling achieves the same accuracy as full retraining of the model using only 49% of the computational cost, and utilises only the embedding vectors of the faces rather than the raw face images. A fairness audit of the system on the FairFace dataset reveals a null result in relation to the fairness metric, suggesting future work on this topic. All experiments utilised publicly available datasets and models, and the code for implementing the proposed methods is provided as a publicly available repository.
Behavioral Economics and Consumer Behavior:
(Crown Publishing, 2025) Siddharth Jh.
Behavioral Economics and Consumer Behavior: Understanding the Mind of the Modern Consumer by Siddharth Jha and Olena Sokhatska examines how psychological, emotional, social, cultural, and situational factors influence consumer decision-making. Challenging the traditional assumption that consumers behave as fully rational agents, the book explains how cognitive biases, heuristics, bounded rationality, loss aversion, present bias, and emotions produce systematic and predictable patterns of behavior. It traces the historical development of behavioral economics through the contributions of Daniel Kahneman, Amos Tversky, and Richard Thaler, with particular attention to Prospect Theory and Nudge Theory. The authors connect these concepts with traditional consumer-behavior models and the five-stage purchasing process. The book also demonstrates their practical application in pricing, promotional campaigns, personalization, gamification, product design, user experience, and public policy. Examples involving retirement savings, organ donation, healthcare, and digital services illustrate how choice architecture can guide behavior without eliminating freedom of choice. Finally, the book considers the growing role of artificial intelligence, big data, and predictive analytics while addressing challenges involving cultural differences, privacy, manipulation, and paternalism. It concludes that behavioral interventions should be transparent, reversible, empirically tested, culturally sensitive, and aligned with consumers’ interests.
Enhancing Students' Interpersonal Skills in Secondary School Mathematics Education
(SDU University, 2026) Ongarbek A.
This study investigates the development of students’ interpersonal interaction skills in secondary school mathematics education. The relevance of the research is determined by the growing need to enhance students’ communication, collaboration, and mathematical discourse skills in accordance with contemporary educational requirements. In mathematics lessons, students are expected not only to solve tasks correctly, but also to explain their reasoning, justify their ideas, listen to peers, and participate in meaningful discussion. The purpose of the study was to identify effective pedagogical conditions for developing interpersonal interaction skills among secondary school students in mathematics education and to test their effectiveness experimentally on a sample of seventh-grade students. The research employed theoretical and empirical methods, including analysis of pedagogical literature, classroom observation, questionnaires, pedagogical experimentation, and qualitative analysis of the obtained results. A methodological system based on dialogic teaching, collaborative learning, communicative tasks, and psychotype-based group organization was developed and implemented. The results of the study showed that the proposed methodology had a positive impact on students’ communication, cooperation, active participation, confidence in expressing ideas, and ability to use mathematical language during lessons. The scientific novelty of the study lies in the development and experimental validation of a psychotype-based model for organizing collaborative learning in mathematics lessons. The practical significance of the research is associated with the possibility of applying the proposed instructional strategies and communicative tasks in secondary school mathematics education.
AI-Powered Technology in Enhancing Biology Education
(SDU University, 2026) Bizhanova A.
This study investigated the impact of AI-supported learning on academic achievement, student perceptions, engagement, independent learning, and AI literacy in university biology education. The research was conducted among 52 undergraduate students enrolled in a biology course focusing on nervous system anatomy. A quasiexperimental design was employed, involving a control group receiving traditional instruction and an experimental group participating in AI-supported learning activities. Data were collected using pre-tests, post-tests, and a student perception questionnaire. The results demonstrated significant improvements in academic achievement within both groups. Although the AI-supported group achieved higher mean post-test scores, the difference between groups was not statistically significant according to independent-samples t-tests and ANCOVA analyses. Questionnaire findings revealed highly positive student perceptions of AI-supported learning. Students reported that AI facilitated understanding of complex biological concepts, supported independent learning, enhanced engagement, and helped identify knowledge gaps. Participants also demonstrated awareness of AI-related challenges, including misinformation, inaccuracies, and excessive reliance on AI-generated content. The findings suggest that AI-supported learning represents a valuable complement to traditional biology instruction. While no statistically significant achievement advantage was identified, AI contributed positively to student engagement, independent learning, conceptual understanding, and responsible.
Investigating the Effect of Peer Learning on Students' Achievement and Attitude in "Discrete Mathematics"
(SDU University, 2026) Orazkhan A.
This research investigated how the Think–Pair–Share method combined with Structured Problem Solving (TPS–SPS) impacted students' academic performance and their attitudes about Discrete Mathematics. The study used a quasi-experimental pretest/posttest non-equivalent control group design with second- and third- year mathematics pedagogy students at a private university located in the Almaty region of Kazakhstan (N = 164 for achievement; N = 72 for attitude). The participants of the study attended the same lectures, yet their practice sessions differed because experimental groups completed TPS–SPS activities during a three-week Counting and Probability unit while control groups followed traditional individual-work instruction. Academic achievement was measured using a researcher-developed open-ended test (α = 0.809), and attitudes were assessed using an adapted Attitudes Toward Mathematics Inventory (α = 0.909). ANCOVA results showed a statistically significant effect for the 2nd-year cohort, with the experimental group producing higher adjusted post-test scores. The 3rd-year cohort did not show a statistically significant effect; however, both cohorts displayed similar improvement patterns, with experimental groups showing higher gains. No significant between-group differences were found in overall attitude for either cohort, yet the 2nd-year experimental group demonstrated a significant within-group improvement in self-confidence. The results suggest that TPS-SPS is an effective method for enhancing Discrete Mathematics achievement, while short-term interventions produce limited attitudinal change.