Content uploaded by Derek Lomas
Author content
All content in this area was uploaded by Derek Lomas on May 09, 2017
Content may be subject to copyright.
A preview of the PDF is not available
Is Difficulty Overrated? The Effects of Choice, Novelty and Suspense on Intrinsic Motivation in Educational Games
Abstract and Figures
Instructional designers often turn to theories of motivational design when producing educational games. The popular theory of ”Flow” predicts that game motivation will be greatest when games are ”not too hard and not too easy.” We tested this idea by randomly assigning >20,000 learners to variations of a popular educational math game. Results: In almost all cases, increases in game difficulty reduced motivation. In contrast, increasing ”novelty” and ”suspense” enhanced motivation. Implications: Difficulty may be overrated. Novelty and suspense are way more fun! Perhaps the maxim should be ”not too hard and not too boring”. More generally, this work shows how product experiments (A/B testing) can improve both scientific theory and student outcomes at scale.
Figures - uploaded by Derek Lomas
Author content
All figure content in this area was uploaded by Derek Lomas
Content may be subject to copyright.
Research in learning technologies is often focused on optimizing some aspects of human learning. However, the usefulness of practical learning environments is heavily influenced by their weakest aspects, and, unfortunately, there are many things that can go wrong in the learning process. In this article, we argue that in many circumstances, it is more useful to focus on avoiding stupidity rather than seeking optimality. To make this perspective specific and actionable, we propose a definition of stupidity, a taxonomy of undesirable behaviors of learning environments, and an overview of data-driven techniques for finding defects. The provided overview is directly applicable in the development of learning environments and also provides inspiration for novel research directions and novel applications of existing techniques.
Research on game-based learning (GBL) methods shows that they may increase students' motivation and learning in the context of higher education. However, there is still unclarity regarding whether and how GBL methods can be utilized in project management education. Our quasi-experimental study analyzes project management students' experiences of a GBL method applied in six European higher education institutes during late 2021 and early 2022. Data about students' experiences were collected using a post-game survey in which students were asked to evaluate how the applied GBL method affected their motivation and learning. The data were analyzed using both qualitative and quantitative methods. Our findings include students' positive and negative perceptions related to the applied GBL method, which influenced students’ motivation to study and learn project management phenomena. Our findings indicate that game-based learning solutions can be used to motivate students and to prepare learners to deal with uncertainty, as in real-life projects.
A detailed illustration of how large scale digital learning systems can incrementally reduce the poverty-achievement gap
Learning to read is one of the most important achievements of early childhood, and sets the stage for future success. Even prior to school entry, children’s foundational literacy skills predict their later academic trajectories (Duncan et al., 2007; La Paro & Pianta, 2000; Lloyd, 1969; Lloyd, 1978). Children learn to read with differing levels of ease, with an estimated 5-17% of school-age children who struggle with reading acquisition (Shaywitz, 1998). The individual variation in children’s reading skills can be predicted by genetic, environmental, academic and socio-demographic factors (for review, see Peterson & Pennington, 2015). This chapter focuses on the relationship between reading development and socioeconomic status (SES), with attention to both cognitive outcomes and neural mechanisms. First, we describe SES and its relation to academic achievement in general, and reading development in particular. Second, we examine environmental factors that can potentially give rise to socioeconomic disparities in reading, such as early language/literacy exposure and access to books. Next, we explore the link between SES and reading disability (RD), including a focus on intervention approaches and treatment response. Finally, we summarize remaining questions and propose future research priorities.
Durante las últimas dos décadas, la investigación en el área de estudio de la pobreza ha comenzado a aportar evidencia que constituye un avance en la comprensión de cómo la adversidad temprana asociada a privaciones materiales, sociales y culturales modula el desarrollo cerebral. Cuando tal evidencia es utiliada en otros contextos disciplinares, con frecuencia se verifican referencias al desarrollo cerebral temprano como a un factor predictor de conductas adaptativas y de productividad económica durante la vida adulta; o de la imposibilidad de tales logros por la supuesta inmutabilidad de los impactos negativos a largo plazo de la pobreza infantil. Este tipo de afirmaciones, que tienen implicaciones no solo científicas sino también políticas, requieren ser analizadas de manera adecuada a la luz de la evidencia disponible, ya que podrían inducir conceptos erróneos y sobre-eneralizaciones que tienen la potencialidad de afectar los criterios para la inversion, el diseño, la implementación y la evaluación de acciones en el ámbito de la infancia temprana. Este libro se propone como un aporte en esta dirección. Los distintos capítulos, a cargo de referentes destacados del estudio neurocientífico y cognitivo de la pobreza, aportan evidencia que alimenta hipótesis y reflexiones en línea con las principales preguntas de tal área de estudio.
"Multi-armed bandits" offer a new paradigm for the AI-assisted design of user interfaces. To help designers understand the potential, we present the results of two experimental comparisons between bandit algorithms and random assignment. Our studies are intended to show designers how bandits algorithms are able to rapidly explore an experimental design space and automatically select the optimal design configuration. Our present focus is on the optimization of a game design space. The results of our experiments show that bandits can make data-driven design more efficient and accessible to interface designers, but that human participation is essential to ensure that AI systems optimize for the right metric. Based on our results, we introduce several design lessons that help keep human design judgment in the loop. We also consider the future of human-technology teamwork in AI-assisted design and scientific inquiry. Finally, as bandits deploy fewer low-performing conditions than typical experiments, we discuss ethical implications for bandits in large-scale experiments in education.