Metacognition analysis of five grade students in elementary school on numbers

This study aimed to observe student learning activities in terms of number material metacognition. The method is a case study qualitative research. The research subjects were 30 students of the fifth-grade elementary school in Malang. Data collection techniques use tests and interviews by carrying out three stages: planning, monitoring, and evaluation. The results showed that internal and external factors caused 75% of moderate learning difficulties. Internal factors arise due to a lack of understanding of mathematical concepts, lack of thoroughness, and interest in literacy. Meanwhile, the external media used by the teacher was deemed less supportive, and the class conditions were not conducive. Thus, there is a conclusion that internal and external factors cause students’ metacognition analysis

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

Exploring the Effects of the Computational Experiment Approach to the Epistemic Beliefs, the Motivation, the Use of Modeling Indicators and Conceptual Understanding in Three Different Computational Learning Environments

Contemporary teaching and learning approaches expect students -at any level of education- to be active producers of knowledge. This leads to the need for creation of instructional strategies, learning environments and tasks that can offer students opportunities for active learning. Research argues that one of the most meaningful and engaging forms of active learning is modelling, that is using computational environments to build representational and epistemological models of the phenomena that are being studied, following the essential features of inquiry based science and mathematics education. The aim of the article is to investigate the impact of the computational experiment approach on: a) students’ learning performance, b) the use of modelling indicators c) the motives and d) the scientific epistemic beliefs. K-12 students in a Greek school participated in the experiment and the methodology of the computational experiment was implemented using three software tools, namely: the Modellus software, the Interactive Physics (IP) software and the Easy Java Simulator Software (EJS), in order to study the concept of the simple harmonic motion (equations of motion, dynamics and energy consideration). All applications were developed using the essential features of inquiry based science education in the framework of the computational approach methodology and the model was considered as the fundamental instruction unit. The findings suggest that the inquiry based computational experiment using Easy Java Simulator(EJS) is more effective in enhancing the conceptual understanding of students as well as the use of modelling indicators, and these results may have implications for the effectiveness of the computational experiment methodology when it is combined with the EJS Tools

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Retrieval-, Distributed-, and Interleaved Practice in the Classroom:A Systematic Review

Three of the most effective learning strategies identified are retrieval practice, distributed practice, and interleaved practice, also referred to as desirable difficulties. However, it is yet unknown to what extent these three practices foster learning in primary and secondary education classrooms (as opposed to the laboratory and/or tertiary education classrooms, where most research is conducted) and whether these strategies affect different students differently. To address these gaps, we conducted a systematic review. Initial and detailed screening of 869 documents found in a threefold search resulted in a pool of 29 journal articles published from 2006 through June 2020. Seventy-five effect sizes nested in 47 experiments nested in 29 documents were included in the review. Retrieval- and interleaved practice appeared to benefit students’ learning outcomes quite consistently; distributed practice less so. Furthermore, only cognitive Student*Task characteristics (i.e., features of the student’s cognition regarding the task, such as initial success) appeared to be significant moderators. We conclude that future research further conceptualising and operationalising initial effort is required, as is a differentiated approach to implementing desirable difficulties

Bringing Nordic mathematics education into the future : Preceedings of Norma 20 : The ninth Nordic conference on mathematics education Oslo, 2021

This volume presents Nordic mathematics education research, which will be presented at the Ninth Nordic Conference on Mathematics Education, NORMA 20, in Oslo, Norway, in June 2021. The theme of NORMA 20 regards what it takes or means to bring Nordic mathematics education into the future, highlighting that mathematics education is continuous and represents stability just as much as change.publishedVersio