An Identification of Students’ Responses Based on Solo Taxonomy in Mathematics Learning Toward Learning Activities and Learning Outcomes

Taxonomy solo is a classification of real responses from students. This research aims to identify the effect of students' responses based on a solo taxonomy in mathematics learning on learning activity and learning outcomes. This research is a mixed-method with an explanatory sequential design. The data were collected using observation instruments, questionnaires, interviews, and tests. The data was analyzed inferentially and narratively. Based on the results, students who are at the extended abstract response level are classified as very active and having very high learning outcomes, students who are at the relational response level are identified as active and having high learning outcomes, students who are at the multi-structural response level are identified as active and having moderate learning outcomes, students who are at the Uni-structural response level are identified as active and having moderate learning outcomes, and students who are at the pre-structural response level are identified as less active and having low learning outcomes. Thus, it can be interpreted that students' responses based on solo taxonomy in mathematics learning affect activeness and learning outcomes.

Análise dos Perfis de Alunos do Ensino Superior sobre a Realização de Aulas na Modalidade a Distância Durante Pandemia da Covid-19 Usando Algoritmos de Aprendizagem de Máquina

Este artigo propõe analisar perfis de alunos do ensino superior sobre o ensino na modalidade a distância durante a pandemia da Covid-19 usando mineração de dados. A metodologia de pesquisa utilizada foi exploratória composta por 5 fases: entendimento do problema, construção de um formulário para coleta de dados, pré-processamento dos dados, aplicação de algoritmos de aprendizagem de máquina e avaliação dos resultados. Os resultados encontrados identificaram grupos de alunos com características distintas contendo diferentes opiniões sobre a aplicação do ensino a distância. Foi possível classificar os perfis dos alunos por meio de uma análise dos atributos mais relevantes em cada um dos grupos

The Upstream Sources Of Bias: Investigating Theory, Design, And Methods Shaping Adaptive Learning Systems

Adaptive systems in education need to ensure population validity to meet the needs of all students for an equitable outcome. Recent research highlights how these systems encode societal biases leading to discriminatory behaviors towards specific student subpopulations. However, the focus has mostly been on investigating bias in predictive modeling, particularly its downstream stages like model development and evaluation. My dissertation work hypothesizes that the upstream sources (i.e., theory, design, training data collection method) in the development of adaptive systems also contribute to the bias in these systems, highlighting the need for a nuanced approach to conducting fairness research. By empirically analyzing student data previously collected from various virtual learning environments, I investigate demographic disparities in three cases representative of the aspects that shape technological advancements in education: 1) non-conformance of data to a widely-accepted theoretical model of emotion, 2) differing implications of technology design on student outcomes, and 3) varying effectiveness of methodological improvements in annotated data collection. In doing so, I challenge implicit assumptions of generalizability in theory, design, and methods and provide an evidence-based commentary on future research and design practices in adaptive and artificially intelligent educational systems surrounding how we consider diversity in our investigations

Active Learning for Student Affect Detection

“Sensor-free” detectors of student affect that use only student activity data and no physical or physiological sensors are cost-effective and have potential to be applied at large scale in real classrooms. These detectors are trained using student affect labels collected from human observers as they observe students learn within intelligent tutoring systems (ITSs) in real classrooms. Due to the inherent diversity of student activity and affect dynamics, observing the affective states of some students at certain times is likely to be more informative to the affect detectors than observing others. Therefore, a carefully-crafted observation schedule may lead to more meaningful observations and improved affect detectors. In this paper, we investigate whether active (machine) learning methods, a family of methods that adaptively select the next most informative observation, can improve the efficiency of the affect label collection process. We study several existing active learning methods and also propose a new method that is ideally suited for the problem setting in affect detection. We conduct a series of experiments using a real-world student affect dataset collected in real classrooms deploying the ASSISTments ITS. Results show that some active learning methods can lead to high-quality affect detectors using only a small number of highly informative observations. We also discuss how to deploy active learning methods in real classrooms to improve the affect label collection process and thus sensor-free affect detectors