Characterizing Productive Perseverance Using Sensor-Free Detectors of Student Knowledge, Behavior, and Affect

Failure is a necessary step in the process of learning. For this reason, there has been a myriad of research dedicated to the study of student perseverance in the presence of failure, leading to several commonly-cited theories and frameworks to characterize productive and unproductive representations of the construct of persistence. While researchers are in agreement that it is important for students to persist when struggling to learn new material, there can be both positive and negative aspects of persistence. What is it, then, that separates productive from unproductive persistence? The purpose of this work is to address this question through the development, extension, and study of data-driven models of student affect, behavior, and knowledge. The increased adoption of computer-based learning platforms in real classrooms has led to unique opportunities to study student learning at both fine levels of granularity and longitudinally at scale. Prior work has leveraged machine learning methods, existing learning theory, and previous education research to explore various aspects of student learning. These include the development of sensor-free detectors that utilize only the student interaction data collected through such learning platforms. Building off of the considerable amount of prior research, this work employs state-of-the-art machine learning methods in conjunction with the large scale granular data collected by computer-based learning platforms in alignment with three goals. First, this work focuses on the development of student models that study learning through the use of advancements in student modeling and deep learning methodologies. Second, this dissertation explores the development of tools that incorporate such models to support teachers in taking action in real classrooms to promote productive approaches to learning. Finally, this work aims to complete the loop in utilizing these detector models to better understand the underlying constructs that are being measured through their application and their connection to productive perseverance and commonly-observed learning outcomes

The (Surprising) Efficacy of Academic and Behavioral Intervention with Disadvantaged Youth : Results from a Randomized Experiment in Chicago

There is growing concern that improving the academic skills of disadvantaged youth is too difficult and costly, so policymakers should instead focus either on vocationally oriented instruction for teens or else on early childhood education. Yet this conclusion may be premature given that so few previous interventions have targeted a potential fundamental barrier to school success: "mismatch" between what schools deliver and the needs of disadvantaged youth who have fallen behind in their academic or non-academic development. This paper reports on a randomized controlled trial of a two-pronged intervention that provides disadvantaged youth with non-academic supports that try to teach youth social-cognitive skills based on the principles of cognitive behavioral therapy (CBT), and intensive individualized academic remediation. The study sample consists of 106 male 9th and 10th graders in a public high school on the south side of Chicago, of whom 95% are black and 99% are free or reduced price lunch eligible. Participation increased math test scores by 0.65 of a control group standard deviation (SD) and 0.48 SD in the national distribution, increased math grades by 0.67 SD, and seems to have increased expected graduation rates by 14 percentage points (46%). While some questions remain about the intervention, given these effects and a cost per participant of around $4,400 (with a range of$3,000 to $6,000), this intervention seems to yield larger gains in adolescent outcomes per dollar spent than many other intervention strategies

Evaluation of the effect of a digital mathematics game on academic achievement

Digital games are widely popular and interest has increased for their use in education. Digital games are thought to be powerful instructional tools because they promote active learning and feedback, provide meaningful contexts to situate knowledge, create engagement and intrinsic motivation, and have the ability individualize instruction. However, claims about the potential benefits of digital games in education have outpaced quality empirical research on their effectiveness in K-12 settings. The purpose of this study was to investigate the effectiveness of a mathematics digital game, Ko’s Journey, on seventh grade students’ mathematics achievement as defined by a researcher-constructed test aligned with the Common Core Mathematics Standards (National Governors Association Center for Best Practices, Council of Chief State School Officers, 2010) and measured on a Rasch (1960) unidimensional equal-interval scale. This research was conducted using secondary data from a pretest-posttest control group design study with a total of 371 seventh grade students from 10 classrooms. Classroom teachers randomly assigned their classroom sections to play the mathematics digital game or served as a wait-listed control group and continued using the typical mathematics curriculum. Hierarchical linear modeling (HLM) and Rasch differential item functioning were used to determine the effect of the intervention on student’s mathematics achievement. Hierarchical linear modeling analyses, using person ability logit estimates derived from the Rasch scaling, concluded that the Ko’s Journey intervention did not have a significant effect on posttest scores. The HLM analyses revealed a significant positive relationship between the students’ individual pretest and posttest scores and the classroom average pretest and posttest scores. Using the Rasch differential item functioning, six assessment items were significantly less difficult for the experimental group compared to the control group; this suggested that the intervention was successful in teaching the mathematics targeted by the items. Technological problems experienced in the classrooms and differential implementation of the game among teachers confounded an accurate estimate of the efficacy of the digital game to improve academic achievement

Inducing self-explanation: A meta-analysis and experiment

Self-explanation is a process by which learners generate inferences about causal connections or conceptual associations. This dissertation seeks to contribute to the literature on inducing self-explanations, by way of prompting, to facilitate learning. More specifically, this research seeks to understand the effects on learning gains when learners are prompted to self-explain in various contexts and with various prompts. As such, one goal of the dissertation is to provide a comprehensive review of prior research on self-explanation. A meta-analysis was conducted on research that investigated learning outcomes of participants who received self-explanation prompts while studying or solving problems. Our systematic search of relevant bibliographic databases identified 69 effect sizes (from 64 research reports) which met certain inclusion criteria. The overall weighted mean effect size using a random effects model was g = .55. We coded and analyzed 20 moderator variables including type of learning task (e.g., solving problems, studying worked problems, and studying text), subject area, level of education, type of inducement, and treatment duration. We found that self-explanation prompts (SEPs) are a potentially powerful intervention across a range of instructional conditions. To further investigate the effect of various prompts on studying expository text, I conducted an online experiment employing a 2 x 2 x 3 factorial design, in which one factor was within subject. One hundred and twenty-six participants were randomly assigned to one of three self-explanation prompt conditions (content-free (generic), content-specific (specific), and no SEP). The results support the utilization of generic self-explanation prompts in comparison to specific self-explanation prompts and receiving no prompt. Specifically, the generic self-explanation group outperformed the other two groups on the reading comprehension outcome in the short-answer question format

Let’s tweet again? Social networks and literature achievement in high school students

The availability of cheap Wi-Fi internet connections has encouraged schools to adopt Web 2.0 platforms for teaching, with the intention of stimulating students’ academic achievement and participation in school. Moreover, during the recent explosion of the SARS-CoV-2 crisis that forced many countries to close schools (as well as offices and factories), the widespread diffusion of these applications kept school systems going. Despite their widespread use as teaching tools, the effect of adopting Web 2.0 platforms on students’ performance has never been rigorously tested. We fill this gap in the literature by analyzing the impact of using Twitter as a teaching tool on high school students’ literature skills. Based on a large-scale, randomized controlled trial that involved 70 schools and about 1,500 students, we find that using Twitter to teach literature has an overall negative effect on students’ average achievement, reducing standardized test scores by about 25 percent of a standard deviation. The negative effect is stronger on students who usually perform better