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Across three experiments featuring naturalistic concepts (psychology concepts) and naïve learners, we extend previous research showing an effect of the sequence of study on learning outcomes, by demonstrating that the sequence of examples during study changes the representation the learner creates of the study materials. We compared participants' performance in test tasks requiring different representations and evaluated which sequence yields better learning in which type of tests. We found that interleaved study, in which examples from different concepts are mixed, leads to the creation of relatively interrelated concepts that are represented by contrast to each other and based on discriminating properties. Conversely, blocked study, in which several examples of the same concept are presented together, leads to the creation of relatively isolated concepts that are represented in terms of their central and characteristic properties. These results argue for the integrated investigation of the benefits of different sequences of study as depending on the characteristics of the study and testing situation.

 

In this study, we examined students’ natural studying behaviors in massive, open, online course (MOOC) on introductory psychology. We found that, overall, distributing study across multiple sessions—increasing spacing—was related to increased performance on end-of-unit quizzes, even when comparing the same student across different time-points in the course. Moreover, we found important variation on who is more likely to engage in spaced study and benefit from it. Students with higher ability and students who were more likely to complete course activities were more likely to space their study. Spacing benefits, however, were largest for the lower-ability students and for those students who were less likely to complete activities. These results suggest that spaced study might work as a buffer, improving performance for low ability students and those who do not engage in active practices. This study highlights the positive impact of spacing in real-world learning situations, but more importantly, the role of self-regulated learning decisions in shaping the impact of spaced practice.

 

Abstract What we learn about the world is affected by the input we receive. Many extant category learning studies use uniform distributions as input in which each exemplar in a category is presented the same number of times. Another common assumption on input used in previous studies is that exemplars from the same category form a roughly normal distribution. However, recent corpus studies suggest that real-world category input tends to be organized around skewed distributions. We conducted three experiments to examine the distributional properties of the input on category learning and generalization. Across all studies, skewed input distributions resulted in broader generalization than normal input distributions. Uniform distributions also resulted in broader generalization than normal input distributions. Our results not only suggest that current category learning theories may underestimate category generalization but also challenge current theories to explain category learning in the real world with skewed, instead of the normal or uniform distributions often used in experimental studies. 

Simulations of human learning have shown potential for supporting ITS authoring and testing, in addition to other use cases. To date, simulated learner technologies have often failed to robustly achieve perfect performance with considerable training. In this work we identify an impediment to producing perfect asymptotic learning performance in simulated learners and introduce one significant improvement to the Apprentice Learner Framework to this end. 

Given the demonstrated prevalence of a “doer effect” showing that active practice is related to substantially larger learning gains than passive approaches, an important research goal is to investigate whether and how different active practice features promote students’ learning outcomes. We investigated these questions in the context of an online learning platform that teaches e-learning design principles. In particular, we considered two different practice modes -practice activities inserted in the text (inline practice) and review practice quizzes -and compared their contributions to students' learning outcomes, in terms of module quizzes, periodic exams, and course projects. Our results showed that the different practice modes had distinct impacts on learning outcomes. Doing inline practice activities contributed to students’ quiz performance at the first attempt and project performance while doing review practice quizzes helped students improve their periodic exam performance. We offer some instructional suggestions such as emphasizing practice activities that are more clearly linked with specific learning objectives for projects, and emphasizing review practice quizzes for exam preparation. 

Intelligent tutoring systems (ITSs) have consistently been shown to improve the educational outcomes of students when used alone or combined with traditional instruction. However, building an ITS is a time-consuming process which requires specialized knowledge of existing tools. Extant authoring methods, including the Cognitive Tutor Authoring Tools' (CTAT) example-tracing method and SimStudent's Authoring by Tutoring, use programming-by-demonstration to allow authors to build ITSs more quickly than they could by hand programming with model-tracing. Yet these methods still suffer from long authoring times or difficulty creating complete models. In this study, we demonstrate that Simulated Learners built with the Apprentice Learner (AL) Framework can be combined with a novel interaction design that emphasizes model transparency, input flexibility, and problem solving control to enable authors to achieve greater model completeness in less time than existing authoring methods.