Characterizing Student Engagement in a Post-Secondary Developmental Mathematics Class and Exploring the Reflexivity between Social and Sociomathematical Norms

Traditionally, post-secondary developmental mathematics courses aspire to equip students with mathematical content knowledge needed to succeed in calculus and subsequent STEM courses. The literature shows that this goal alone is insufficient, as the emphasis on content acquisition often comes at the expense of developing higher-order skills such as argumentation, reasoning, and flexibility in mathematics problem solving (Chiaravalloti, 2009; Partanen & Kaasila, 2014; Star et al., 2015). Redesigning curricula with these additional objectives in mind requires providing students with opportunities to engage with mathematics in ways that may contrast with their past experiences or expectations. It requires changing patterns of classroom engagement and development of different classroom norms. This mixed methods research study incorporated a semester-long teaching experiment that aimed to support students\u27 development of higher-order skills by negotiating productive classroom norms. One of the primary interventions was a sequence of Multiple Solutions Activities that required groups of students to analyze and critique unfamiliar or erroneous mathematical solutions. The overarching goal of the research was to study students\u27 engagement during these activities across the semester by characterizing the nature of specific types of classroom norms. Social norms describe the classroom participation structure, while sociomathematical norms focus on aspects of student activity that are inherently mathematical, such as what constitutes an acceptable mathematical solution (Yackel & Cobb, 1996). Because of a reflexive relationship between norms and beliefs, students\u27 social and mathematical beliefs were also of interest to characterize the influence of the teaching experiment; these beliefs were assessed by a pre- and post-course questionnaire. The results paint a complex picture of student engagement and values. Despite quantitative analysis suggesting encouraging improvements in students’ mathematical engagement, qualitative analysis highlighted that this change was not homogenous. In particular, the analysis revealed variations in students’ perceptions of the value of multiple solutions and in the nature of the norms developed in student groups. Consequently, the study highlights the lasting impact of classroom norms on students\u27 beliefs, and vice versa, which may hinder the development of alternative norms in subsequent classes. The results of the project also expand upon Yackel and Cobb\u27s (1996) Interpretive Framework for characterizing classroom engagement by suggesting a reflexive relationship exists between social and sociomathematical norms. The data analysis describes concurrent development and mutual influence between the participation structure of a group and their taken-as-shared mathematical beliefs. In all, the project shows that deliberate attention towards negotiating productive classroom norms and students’ in-class engagement can positively affect students’ attitudes towards multiple solutions

Characterizing Student Engagement in a Post-Secondary Developmental Mathematics Class and Exploring the Reflexivity between Social and Sociomathematical Norms

Traditionally, post-secondary developmental mathematics courses aspire to equip students with mathematical content knowledge needed to succeed in calculus and subsequent STEM courses. The literature shows that this goal alone is insufficient, as the emphasis on content acquisition often comes at the expense of developing higher-order skills such as argumentation, reasoning, and flexibility in mathematics problem solving (Chiaravalloti, 2009; Partanen & Kaasila, 2014; Star et al., 2015). Redesigning curricula with these additional objectives in mind requires providing students with opportunities to engage with mathematics in ways that may contrast with their past experiences or expectations. It requires changing patterns of classroom engagement and development of different classroom norms. This mixed methods research study incorporated a semester-long teaching experiment that aimed to support students\u27 development of higher-order skills by negotiating productive classroom norms. One of the primary interventions was a sequence of Multiple Solutions Activities that required groups of students to analyze and critique unfamiliar or erroneous mathematical solutions. The overarching goal of the research was to study students\u27 engagement during these activities across the semester by characterizing the nature of specific types of classroom norms. Social norms describe the classroom participation structure, while sociomathematical norms focus on aspects of student activity that are inherently mathematical, such as what constitutes an acceptable mathematical solution (Yackel & Cobb, 1996). Because of a reflexive relationship between norms and beliefs, students\u27 social and mathematical beliefs were also of interest to characterize the influence of the teaching experiment; these beliefs were assessed by a pre- and post-course questionnaire. The results paint a complex picture of student engagement and values. Despite quantitative analysis suggesting encouraging improvements in students’ mathematical engagement, qualitative analysis highlighted that this change was not homogenous. In particular, the analysis revealed variations in students’ perceptions of the value of multiple solutions and in the nature of the norms developed in student groups. Consequently, the study highlights the lasting impact of classroom norms on students\u27 beliefs, and vice versa, which may hinder the development of alternative norms in subsequent classes. The results of the project also expand upon Yackel and Cobb\u27s (1996) Interpretive Framework for characterizing classroom engagement by suggesting a reflexive relationship exists between social and sociomathematical norms. The data analysis describes concurrent development and mutual influence between the participation structure of a group and their taken-as-shared mathematical beliefs. In all, the project shows that deliberate attention towards negotiating productive classroom norms and students’ in-class engagement can positively affect students’ attitudes towards multiple solutions

In-Context Learning for Few-Shot Molecular Property Prediction

In-context learning has become an important approach for few-shot learning in Large Language Models because of its ability to rapidly adapt to new tasks without fine-tuning model parameters. However, it is restricted to applications in natural language and inapplicable to other domains. In this paper, we adapt the concepts underpinning in-context learning to develop a new algorithm for few-shot molecular property prediction. Our approach learns to predict molecular properties from a context of (molecule, property measurement) pairs and rapidly adapts to new properties without fine-tuning. On the FS-Mol and BACE molecular property prediction benchmarks, we find this method surpasses the performance of recent meta-learning algorithms at small support sizes and is competitive with the best methods at large support sizes

Activities and administration of the TIR Executive Board – Activities of the TIR Executive Board: Report by the Chair of the TIR Executive Board

Item 4 (a) (i) of the provisional agend

The Effect Of Two Stay Two Stray (TSTS) Technique Towards Students’ Reading Comprehension of Descriptive Text of The Second Year Students at SMPN 4 Siak Hulu

The objective of this research was to find out whether there is any significant effect of Two Stay Two Stray (TSTS) technique towards students’ reading comprehension of descriptive text of the second year students at SMPN 4 Siak Hulu. This research was experimental research. The population of this research was the second year students at SMPN 4 Siak Hulu. The sample of this research were divided into two classes: class X VIII 6 as experimental class and class VIII 7 as control class. The instrument for collecting the data was reading test of descriptive text by using TSTS technique. The data were in the forms of pre-test and post-test scores. The pre-test was given to both classes before the treatment and the post-test was given at the end of the treatment. During this research, the students of the experimental class were taught by using TSTS technique, while the students of control class were taught without using TSTS technique. The data were analyzed by using independent sample t-test from SPSS 24 program. The data of both pre-test and post-test from experimental class and control class were also compared. The result of One Way Anova test showed that Ha is accepted and Ho is rejected. The significant probability higher than 0.05 (P> 0.05) which was 0.027. In other word, there is significant difference on students’ reading comprehension in post-test score between experimental group and control group. So, it can be concluded that there is a significant effect of Two Stay Two Stray (TSTS) technique towards students’ reading comprehension of descriptive text of the second year students at SMPN 4 Siak Hulu

Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction

Few-shot learning is a promising approach to molecular property prediction as supervised data is often very limited. However, many important molecular properties depend on complex molecular characteristics -- such as the various 3D geometries a molecule may adopt or the types of chemical interactions it can form -- that are not explicitly encoded in the feature space and must be approximated from low amounts of data. Learning these characteristics can be difficult, especially for few-shot learning algorithms that are designed for fast adaptation to new tasks. In this work, we develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations, and a multi-task learning paradigm to structure the embedding space. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance. Our code is available at https://github.com/cfifty/IGNITE