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

Cross-Modal Attention Influences Auditory Contrast Sensitivity: Decreasing Visual Load Improves Auditory Thresholds for Amplitude- and Frequency-Modulated Sounds

We used a cross-modal dual task to examine how changing visual-task demands influenced auditory processing, namely auditory thresholds for amplitude- and frequency-modulated sounds. Observers had to attend to two consecutive intervals of sounds and report which interval contained the auditory stimulus that was modulated in amplitude (Experiment 1) or frequency (Experiment 2). During auditory-stimulus presentation, observers simultaneously attended to a rapid sequential visual presentation—two consecutive intervals of streams of visual letters—and had to report which interval contained a particular color (low load, demanding less attentional resources) or, in separate blocks of trials, which interval contained more of a target letter (high load, demanding more attentional resources). We hypothesized that if attention is a shared resource across vision and audition, an easier visual task should free up more attentional resources for auditory processing on an unrelated task, hence improving auditory thresholds. Auditory detection thresholds were lower—that is, auditory sensitivity was improved—for both amplitude- and frequency-modulated sounds when observers engaged in a less demanding (compared to a more demanding) visual task. In accord with previous work, our findings suggest that visual-task demands can influence the processing of auditory information on an unrelated concurrent task, providing support for shared attentional resources. More importantly, our results suggest that attending to information in a different modality, cross-modal attention, can influence basic auditory contrast sensitivity functions, highlighting potential similarities between basic mechanisms for visual and auditory attention

The Mobile Fact and Concept Textbook System (MoFaCTS) computational model and scheduling system

An intelligent textbook may be defined as an interaction layer between the text and the student, helping the student master the content in the text. The Mobile Fact and Concept Training System (MoFaCTS) is an adaptive instructional system for simple content that has been developed into an interaction layer to mediate textbook instruction and so is being transformed into the Mobile Fact and Concept Textbook System (MoFaCTS). In this paper, we document the several terms of the logistic regression model we use to track performance adaptively. We then examine the contribution of each component of our model when it is fit to 4 semesters of Anatomy and Physiology course practice data. Following this documentation of the model, we explain how it is applied in the MoFaCTS system to schedule performance by targeting practice for each item at an optimal efficiency threshold

Optimizing practice scheduling requires quantitative tracking of individual item performance

Decades of research has shown that spacing practice trials over time can improve later memory, but there are few concrete recommendations concerning how to optimally space practice. We show that existing recommendations are inherently suboptimal due to their insensitivity to time costs and individual- and item-level differences. We introduce an alternative approach that optimally schedules practice with a computational model of spacing in tandem with microeconomic principles. We simulated conventional spacing schedules and our adaptive model-based approach. Simulations indicated that practicing according to microeconomic principles of efficiency resulted in substantially better memory retention than alternatives. The simulation results provided quantitative estimates of optimal difficulty that differed markedly from prior recommendations but still supported a desirable difficulty framework. Experimental results supported simulation predictions, with up to 40% more items recalled in conditions where practice was scheduled optimally according to the model of practice. Our approach can be readily implemented in online educational systems that adaptively schedule practice and has significant implications for millions of students currently learning with educational technology