Unbounded Human Learning: Optimal Scheduling for Spaced Repetition

In the study of human learning, there is broad evidence that our ability to retain information improves with repeated exposure and decays with delay since last exposure. This plays a crucial role in the design of educational software, leading to a trade-off between teaching new material and reviewing what has already been taught. A common way to balance this trade-off is spaced repetition, which uses periodic review of content to improve long-term retention. Though spaced repetition is widely used in practice, e.g., in electronic flashcard software, there is little formal understanding of the design of these systems. Our paper addresses this gap in three ways. First, we mine log data from spaced repetition software to establish the functional dependence of retention on reinforcement and delay. Second, we use this memory model to develop a stochastic model for spaced repetition systems. We propose a queueing network model of the Leitner system for reviewing flashcards, along with a heuristic approximation that admits a tractable optimization problem for review scheduling. Finally, we empirically evaluate our queueing model through a Mechanical Turk experiment, verifying a key qualitative prediction of our model: the existence of a sharp phase transition in learning outcomes upon increasing the rate of new item introductions.Comment: Accepted to the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 201

Innovating Pedagogy 2017: Exploring new forms of teaching, learning and assessment, to guide educators and policy makers. Open University Innovation Report 6

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This sixth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Learning In a NetworKed Society (LINKS) Israeli Center of Research Excellence (I-CORE). Themes: • Big-data inquiry: thinking with data • Learners making science • Navigating post-truth societies • Immersive learning • Learning with internal values • Student-led analytics • Intergroup empathy • Humanistic knowledge-building communities • Open Textbooks • Spaced Learnin

Homo economicus in visual search

How do reward outcomes affect early visual performance? Previous studies found a suboptimal influence, but they ignored the non-linearity in how subjects perceived the reward outcomes. In contrast, we find that when the non-linearity is accounted for, humans behave optimally and maximize expected reward. Our subjects were asked to detect the presence of a familiar target object in a cluttered scene. They were rewarded according to their performance. We systematically varied the target frequency and the reward/penalty policy for detecting/missing the targets. We find that 1) decreasing the target frequency will decrease the detection rates, in accordance with the literature. 2) Contrary to previous studies, increasing the target detection rewards will compensate for target rarity and restore detection performance. 3) A quantitative model based on reward maximization accurately predicts human detection behavior in all target frequency and reward conditions; thus, reward schemes can be designed to obtain desired detection rates for rare targets. 4) Subjects quickly learn the optimal decision strategy; we propose a neurally plausible model that exhibits the same properties. Potential applications include designing reward schemes to improve detection of life-critical, rare targets (e.g., cancers in medical images)

Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling

Adaptive learning technology solutions often use a learner model to trace learning and make pedagogical decisions. The present research introduces a formalized methodology for specifying learner models, Logistic Knowledge Tracing (LKT), that consolidates many extant learner modeling methods. The strength of LKT is the specification of a symbolic notation system for alternative logistic regression models that is powerful enough to specify many extant models in the literature and many new models. To demonstrate the generality of LKT, we fit 12 models, some variants of well-known models and some newly devised, to 6 learning technology datasets. The results indicated that no single learner model was best in all cases, further justifying a broad approach that considers multiple learner model features and the learning context. The models presented here avoid student-level fixed parameters to increase generalizability. We also introduce features to stand in for these intercepts. We argue that to be maximally applicable, a learner model needs to adapt to student differences, rather than needing to be pre-parameterized with the level of each student's ability

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

Human Learning, Memory, and Student Development

My educational interests have largely been informed by my career in the sciences and medicine. My professional education has been both formative and transformative, opening doors to the joy of learning and a realization in the importance of memory. As an educator, clinician, and student, I have been greatly impacted by issues of curricular design, curricular development, learning and memory. My current responsibilities in student affairs also have exposed me to the delicate balance between student development, curricular design, learning and memory. Patton, Renn, Guido, and Quaye (2016) noted the importance of educators being able to use different literature sources and concepts in their daily interactions with students. In addition, Patton et al. (2016) further emphasized the importance of literature in guiding professionals in the development of curricular and related policy changes. Underpinning student development issues is the notion that the goals of education are ultimately tied to memory and learning. Atkinson and Shiffrin (2016) noted that “it is hard to imagine how understanding memory could not be important….memory is what we are, and what defines us as individuals” (p. 115). Atkinson and Shiffrin (2016) further noted how our memory system is divided into structural components and processing components that work together to create a retrievable memory. This simple fact has played out in my professional life as I have provided care for patients with dementia or other memory destroying processes; witnessing the person, father, mother, brother, sister, son, daughter, and friend literally become unrecognizable cognitively. In higher education we understand that learning and memory are symbiotic but not synonymous. Illeris (2018) noted that learning can be defined broadly as any process that leads to “change” and is not solely related to maturation or aging (p. 7). In higher education however, we are more interested in managing student education through the manipulation of learning acquisition and student-environment interactions (Illeris, 2018). Memory is an ill-defined event, which happens in our brain, and is impacted by many external factors (Roediger & Wertsch, 2008). In this paper I will explore issues of student development, human learning, human memory, and how these concepts should inform higher education’s approach to curricular issues and design. I will explore unique learning and memory concepts to provide a better understanding of the many facets of memory and learning. Additionally, I will survey ideas on curricular design that could incorporate important learning and memory concepts

TACHISTOSCOPE ON A VIRTUAL REALITY PLATFORM TO IMPROVE MEMORIZATION AND INCREASE RAPID RECOGNITION

This work investigates whether a tachistoscope on a virtual reality (VR) platform can increase one’s ability to memorize and rapidly recognize objects. Theses abilities are relevant to an array of military requirements. Current procedures mostly utilize flash cards and PowerPoint slides. A tachistoscope (ta-kiss-stow-scope) is an image-flashing device with precise control of the image presentation time. Since the early 1900s they were used to assist with memorization and recognition. One famous example is work done by Renshaw in the 1940s to improve pilots’ ability to recognize tanks, aircraft, and ships (Renshaw, 1945). Our study utilized this technique on modern-day VR and computer platforms. It simplified the use of a tachistoscope and will enable units to customize training packages. This study trained individuals to recognize 40 aircraft over eight training sessions. Training session one began with ten aircraft, and five aircraft were added in each subsequent session. Questions captured three variables: correct/incorrect answer, reaction time, and confidence. Participants were in one of three groups: tachistoscope on a VR platform, tachistoscope on laptop, or computer-based flashcard (control). Results indicate a significant increase in memorization from pretest to posttest for all groups. Furthermore, there was a nonsignificant improvement in reaction time from pretest to posttest across all groups.ONRMajor, United States Marine CorpsApproved for public release. Distribution is unlimited