Cognitive and regulatory characteristics and mathematical performance in high school students.

The study examined the links of cognitive and regulatory characteristics with mathematical outcomes in high school students. Participants were 318 14-16 year old students from 7 state schools in Russia. A computerized test battery was used to measure aspects of number sense, spatial ability, spatial memory and processing speed. The battery also included two measures of mathematical performance. Academic grades and final school test scores in mathematics were also collected. In addition, the students completed the Self-Regulation Profile of Learning Activity Questionnaire – SRPLAQ, which measures different aspects of self-regulation related to achieving learning goals, such as goal planning, results evaluation, and responsibility. The results suggest that cognitive and regulatory features are independently associated with mathematical performance, and that the links differ depending on the specific aspect of mathematical performance used

Learning and Transfer from an Engineering Design Task: The Roles of Goals, Contrasting Cases, and Focusing on Deep Structure

As maker spaces, engineering design curricula, and other hands-on active learning tasks become more popular in science classrooms, it is important to consider what students are intended to take away from these tasks. Many teachers use engineering design tasks as a means of teaching students more general science principles. However, few studies have explored exactly how the design of these activities can support more generalized student learning and transfer. Specifically, research has yet to sufficiently investigate the effects of task design components on the learning and transfer processes that can occur during these kinds of tasks. This dissertation explores how various task manipulations and focusing processes affect how well students can learn and transfers science concepts from an engineering design task. I hypothesized that learning goals that focus students on the deep structure of the problem, and contrasting cases that help students notice that deep structure, would aid learning and transfer. In two experimental studies, students were given an engineering design task. The first study was a 2x2 between subjects design where goal where goal (outcome or learning) and reflection (on contrasting cases or the engineering design process) were manipulated. A subsequent second study then gave all students contrasting cases to reflect on, and only the goal manipulation was manipulated. Results showed that learning goals improved student performance on a transfer task that required students to apply the deep structure to a different engineering design task. In the second study, learning goals improved student performance on a transfer test. Transfer performance in both studies was predicted by the ability to notice the deep structure during the reflection on contrasting cases, even though noticing this structure did not differ by goal condition. Students with a learning goal valued the learning resources they were given more during the engineering design activity, and this perceived value of resources was linked to greater learning. A qualitative case study analysis was then conducted using video data from the second study. This case study investigated noticing processes during the building process, partner dialogue, and resource use. This analysis showed how high transfer pairs were better able to focus on the deep structure of the problem. Results suggest that what students noticed didn’t differ much between the various pairs. However, high transfer pairs were better able to focus on the deep structure through establishing a joint understanding of the deep structure, sustaining concentration on that deep structure during the cases reflection, referencing resources to identify features to test, and then systematically testing those features to identify their relevance. These processes are discussed in relation to how they differ in low transfer pairs. This dissertation consists of four chapters: an intro, two standalone journal articles, and a conclusion. The first chapter provides a conceptual framing for the two journal articles, and discusses the findings from these articles in conversation. The second chapter describes the two empirical studies investigating how task goals and contrasting cases affect learning, and transfer from an engineering design task. The third chapter describes the comparative case study of how mechanisms of focusing on the deep structure differ between high and low transfer pairs. Finally, the fourth conclusion chapter discusses the implications of the work from both of these papers

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Maximising gain for minimal pain: Utilising natural game mechanics

This paper considers the application of natural games mechanics within higher education as a vehicle to encourage student engagement and achievement of desired learning outcomes. It concludes with desiderata of features for a learning environment when used for assessment and a reflection on the gap between current and aspired learning provision. The context considered is higher (tertiary) education, where the aims are both to improve students’ engagement with course content and also to bring about potential changes in the students’ learning behaviour. Whilst traditional approaches to teaching and learning may focus on dealing with large classes, where the onus is frequently on efficiency and on the effectiveness of feedback in improving understanding and future performance, intelligent systems can provide technology to enable alternative methods that can cope with large classes that preserve the cost-benefits. However, such intelligent systems may also offer improved learning outcomes via a personalised learning experience. This paper looks to exploit particular properties which emerge from the game playing process and seek to engage them in a wider educational context. In particular we aim to use game engagement and Flow as natural dynamics that can be exploited in the learning experience