Implicit Theories and Their Role in Judgments and Reactions: A Word From Two Perspectives

In this target article, we present evidence for a new model of individual differences in judgments and reactions. The model holds that people's implicit theories about human attributes structure the way they understand and react to human actions and outcomes. We review research showing that when people believe that attributes (such as intelligence or moral character) are fixed, trait-like entities (an entity theory), they tend to understand outcomes and actions in terms of these fixed traits ('I failed the test because I am dumb' or 'He stole the bread because he is dishonest'). In contrast, when people believe that attributes are more dynamic, malleable, and developable (an incremental theory), they tend to focus less on broad traits and, instead, tend to understand outcomes and actions in terms of more specific behavioral or psychological mediators ('I failed the test because of my effort or strategy' or 'He stole the bread because he was desperate'). The two frameworks also appear to foster different reactions: helpless versus mastery-oriented responses to personal setbacks and an emphasis on retribution versus education or rehabilitation for transgressions. These findings are discussed in terms of their implications for personality, motivation, and social perception.published_or_final_versio

Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments

Randomized experiments ensure robust causal inference that are critical to effective learning analytics research and practice. However, traditional randomized experiments, like A/B tests, are limiting in large scale digital learning environments. While traditional experiments can accurately compare two treatment options, they are less able to inform how to adapt interventions to continually meet learners' diverse needs. In this work, we introduce a trial design for developing adaptive interventions in scaled digital learning environments -- the sequential randomized trial (SRT). With the goal of improving learner experience and developing interventions that benefit all learners at all times, SRTs inform how to sequence, time, and personalize interventions. In this paper, we provide an overview of SRTs, and we illustrate the advantages they hold compared to traditional experiments. We describe a novel SRT run in a large scale data science MOOC. The trial results contextualize how learner engagement can be addressed through inclusive culturally targeted reminder emails. We also provide practical advice for researchers who aim to run their own SRTs to develop adaptive interventions in scaled digital learning environments

The pivotal role of effort beliefs in mediating implicit theories of intelligence and achievement goals & academic motivations

Empirical studies into meaning systems surrounding implicit theories of intelligence typically entail two stringent assumptions: that different implicit theories and different effort beliefs represent opposite poles on a single scale, and that implicit theories directly impact the constructs as achievement goals and academic motivations. Through an empirical study based on a large sample of university students, we aim to demonstrate that relaxing these stringent assumptions, and thereby using the meaning system framework to its full potential, will provide strong benefits: effort beliefs are crucial mediators of relationships between implicit theories and achievement goals and academic motivations, and the different poles of implicit theories and effort beliefs do expose different relationships with goal setting behaviour and academic motivations. A structural equation model, cross-validated by demonstrating gender-invariance of path coefficients, demonstrates that incremental and entity theory views have less predictive power than positive and negative effort beliefs in explaining achievement goals and motivations

Interaction analysis in online maths human tutoring: The case of third space learning

This 'industry' paper reports on the combined effort of researchers and industrial designers and developers to ground the automatic quality assurance of online maths human-to-human tutoring on best practices. We focus on the first step towards this goal. Our aim is to understand the largely under-researched field of online tutoring, to identify success factors in this context and to model best practice in online teaching. We report our research into best practice in online maths teaching and describe and discuss our design and evaluation iterations towards annotation software that can mark up human-to-human online teaching interactions with successful teaching interaction signifiers

Achievement Goal

Achievement goals are self-regulatory commitments that provide direction to individuals as they interpret and respond to competence-relevant situations. Four types of achievement goals have been the primary focus of the literature: Masteryapproach goals (master a task; improve over time), performance-approach goals (outperform others), mastery-avoidance goals (not fall short of mastering a task; not decline over time), and performance-avoidance goals (not be outperformed by others)

Study Effort and Student Success: A MOOC Case Study

Learning was once defined as the function of efforts spent in relation to efforts needed [3]. Provided that effort is closely linked to time, previous research has found a positive relationship between student effort over time and student success, both in university education and Massive Open Online Courses (MOOCs). With the complex environment of tracing and identifying relevant data of student learning processes in MOOCs, this study employs learning analytics to examine this relationship for MITx 6.00x, an introductory programming and computer science MOOC hosted on the edX MOOC platform. A population sample from the MOOC (N = 32,621) was examined using logistic regression, controlling for variables that may also influence the outcome. Conversely, the outcome of this research study suggests that there is a curvilinear relationship between effort over time and student success, meaning those who exert effort for the longest amount of time in the MOOC actually have a lower probability of obtaining a certificate than others who exert effort over somewhat less time. Finally, research implications are discussed.acceptedVersio

Implications of Training in Incremental Theories of Intelligence for Undergraduate Statistics Students

This chapter documents the effects of training in incremental theories of intelligence on students in introductory statistics courses at a liberal arts university in the US. Incremental theories of intelligence examine the beliefs individuals hold of knowledge and how it is attained. An individual with an incremental theory of intelligence believes that intelligence can be developed. The research examined differences by gender in mastery of statistics and attitudes toward statistics for students who received growth mind-set training. A pre-test, post-test design utilised the Students’ Attitudes Toward Statistics© instrument and the Comprehensive Assessment of Outcomes in a first Statistics course. An ANCOVA revealed that females gained more than males on their value of statistics (F(1, 63) 9.40, MSE 3.79, p .003, η2 P 0.134) and decreased less for effort expended to learn statistics (F(1, 63) 4.41, MSE 4.07, p .040, η2 P 0.067). Females also gained mastery of statistical concepts at a greater rate (F(1, 63) 5.30, MSE 0.06, p .025, η2 P 0.080) indicating a possible path to alleviate the under-representation of females in STEM