Handwriting practice is the most time-consuming activity for learners of Chinese as a foreign language (CFL). CFL instructors report allocating at least one third of their course time to handwriting practice although it prevents students from engaging in meaningful communication, especially in the earliest stages of learning. Given the amount of time students spend in a college course is relatively fixed, the preregistered study presented herein examines the best use of students' time when primary goals are word acquisition and communication. This work replicates a pilot study examining CFL word recognition in an online learning environment (ASSISTments) and the effects of supplemental handwriting practice. We examined word acquisition and recognition while manipulating condition (no-handwriting practice and with-handwriting practice), and posttest test point (1 [immediate], 2 [1 day delay], and 3 [1 week delay]). Two-way repeated measures analyses of variance revealed significant main effects for both condition and posttest test point in online and on-paper measures of word recognition and handwriting. Potential implications for CFL instruction and directions for future work are discussed.

Considerable attention has been given to methods for knowledge estimation, a category of methods for automatic assessment of a student's degree of skill mastery or knowledge at a specific time. Knowledge estimation is frequently used to make decisions about when a student has reached mastery and is ready to advance to new material, but there has been little work to forecast how far a student is from mastery or predict how much more practice the student will need before he or she will reach mastery. This article presents a method for predicting the point at which a student will reach skill mastery within an adaptive learning system, based on current approaches to estimating student knowledge. We apply this technique to two popular methods of modeling student learning - Bayesian knowledge tracing and performance factors analysis - and compare prediction correctness. Potential applications and future steps for improving the method are discussed.

The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are only able to provide aid to students who interact with the system. With this in mind, student persistence emerges as a prominent learning construct contributing to students success when learning new material. Conversely, high persistence is not always productive for students, where additional practice does not help the student move toward a state of mastery of the material. In this paper, we apply a transfer learning methodology using deep learning and traditional modeling techniques to study high and low representations of unproductive persistence. We focus on two prominent problems in the fields of educational data mining and learner analytics representing low persistence, characterized as student stopout, and unproductive high persistence, operationalized through student wheel spinning, in an effort to better understand the relationship between these measures of unproductive persistence (i.e., stopout and wheel spinning) and develop early detectors of these behaviors. We find that models developed to detect each within and across-assignment stopout and wheel spinning are able to learn sets of features that generalize to predict the other. We further observe how these models perform at each learning opportunity within student assignments to identify when interventions may be deployed to best aid students who are likely to exhibit unproductive persistence.

A prominent issue faced by the education research community is that of student attrition. While large research efforts have been devoted to studying course-level attrition, widely referred to as dropout, less research has been focused on finer-grained assignment-level attrition commonly observed in K-12 classrooms. This later instantiation of attrition, referred to in this paper as stopout, is characterized by students failing to complete their assigned work, but the cause of such behavior are not often known. This becomes a large problem for educators and developers of learning platforms as students who give up on assignments early are missing opportunities to learn and practice the material which may affect future performance on related topics; similarly, it is difficult for researchers to develop, and subsequently difficult for computer-based systems to deploy interventions aimed at promoting productive persistence once a student has ceased interaction with the software. This difficulty highlights the importance to understand and identify early signs of stopout behavior in order to provide aid to students pre-emptively to promote productive persistence in their learning. While many cases of student stopout may be attributable to gaps in student knowledge and indicative of struggle, student attributes such as grit and persistence may be further affected by other factors. This work focuses on identifying different forms of stopout behavior in the context of middle school math by observing student behaviors at the sub-problem level. We find that students exhibit disproportionate stopout on the first problem of their assignments in comparison to stopout on subsequent problems, identifying a behavior that we call refusal, and use the emerging patterns of student activity to better understand the potential causes underlying stopout behavior early in an assignment.

Identifying the mathematical skills or knowledge components needed to solve a math problem is a laborious task. In our preliminary work, we had two expert teachers identified knowledge components of a state-wide math test and they only agreed only on 35% of the items. Previous research showed that machine learning could be used to correctly tag math problems with knowledge components at about 90% accuracy over more than 100 different skills with five-fold cross-validation. In this work, we first attempted to replicate that result with a similar dataset and were able to achieve a similar cross-validation classification accuracy. We applied the learned model to our test set, which contains problems in the same set of knowledge component definitions, but are from different sources. To our surprise, the classification accuracy dropped drastically from near-perfect to near-chance. We identified two major issues that cause of the original model to overfit to the training set. After addressing the issues, we were able to significantly improve the test accuracy. However, the classification accuracy is still far from being usable in a real-world application.

Online learning environments allow for the implementation of psychometric scales on diverse samples of students participating in authentic learning tasks. One such scale, the Intrinsic Motivation Inventory (IMI) can be used to inform stakeholders of students' subjective motivational and regulatory styles. The IMI is a multidimensional scale developed in support of Self-Determination Theory [1-3], a strongly validated theory stating that motivation and regulation are moderated by three innate needs: autonomy, belonging, and competence. As applied to education, the theory posits that students who perceive volition in a task, those who report stronger connections with peers and teachers, and those who perceive themselves as competent in a task are more likely to internalize the task and excel. ASSISTments, an online mathematics platform, is hosting a series of randomized controlled trials targeting these needs to promote integrated learning. The present work supports these studies by attempting to validate four subscales of the IMI within ASSISTments. Iterative factor analysis and item reduction techniques are used to optimize the reliability of these subscales and limit the obtrusive nature of future data collection efforts. Such scale validation efforts are valuable because student perceptions can serve as powerful covariates in differentiating effective learning interventions.

Research on non-cognitive factors has shown that persistence in the face of challenges plays an important role in learning. However, recent work on wheel-spinning, a type of unproductive persistence where students spend too much time struggling without achieving mastery of skills, show that not all persistence is uniformly beneficial for learning. For this reason, it becomes increasingly pertinent to identify the key differences between unproductive and productive persistence toward informing interventions in computer-based learning environments. In this study, we use a classification model to distinguish between productive persistence and wheel-spinning in ASSISTments, an online math learning platform. Our results indicate that there are two types of students who wheel-spin: first, students who do not request any hints in at least one problem but request more than one bottom-out hint across any 8 problems in the problem set; second, students who never request two or more bottom out hints across any 8 problems, do not request any hints in at least one problem, but who engage in relatively short delays between solving problems of the same skill. These findings suggest that encouraging students to both engage in spaced practice and use bottom-out hints sparingly is likely helpful for reducing their wheel-spinning and improving learning. These findings also provide insight on when students are struggling and how to make students' persistence more productive.

Background: Interactive learning environments often provide help strategies to facilitate learning. Hints, for example, help students recall relevant concepts, identify mistakes, and make inferences. However, several studies have shown cases of ineffective help use. Findings from an initial study on the availability of hints in a mathematics problem-solving activity showed that early access to on-demand hints were linked to lack of performance improvements and longer completion times in students answering problems for summer work. The same experimental methodology was used in the present work with a different student sample population collected during the academic year to check for generalizability. Results: Results from the academic year study showed that early access to on-demand-hints in an online mathematics assignment significantly improved student performance compared to students with later access to hints, which was not observed in the summer study. There were no differences in assignment completion time between conditions, which had been observed in the summer study and has been attributed to engagement in off-task activities. Although the summer and academic year studies were internally valid, there were significantly more students in the academic year study who did not complete their assignment. The sample populations differed significantly by student characteristics and external factors, possibly contributing to differences in the findings. Notable contextual factors that differed included prior knowledge, grade level, and assignment deadlines. Conclusions: Contextual differences influence hint effectiveness. This work found varying results when the same experimental methodologywas conducted on two separate sample populations engaged in different learning settings. Further work is needed, however, to better understand how on-demand hints generalize to other learning contexts. Despite its limitations, the study shows how randomized controlled trials can be used to better understand the effectiveness of instructional designs applied in online learning systems that cater to thousands of learners across diverse student populations. We hope to encourage additional research that will validate the effectiveness of instructional designs in different learning contexts, paving the way for the development of robust and generalizable designs.

Affect detection has become a prominent area in student modeling in the last decade and considerable progress has been made in developing effective models. Many of the most successful models have leveraged physical and physiological sensors to accomplish this. While successful, such systems are difficult to deploy at scale due to economic and political constraints, limiting the utility of their application. Examples of sensor-free affect detectors that assess students based solely using data on the interaction between students and computer-based learning platforms exist, but these detectors generally have not reached high enough levels of quality to justify their use in real-time interventions. However, the classification algorithms used in these previous sensor-free detectors have not taken full advantage of the newest methods emerging in the field. The use of deep learning algorithms, such as recurrent neural networks (RNNs), have been applied to a range of other domains including pattern recognition and natural language processing with success, but have only recently been attempted in educational contexts. In this work, we construct new deep sensor-free affect detectors and report significant improvements over previously reported models.

Although gaming the system, a behavior in which students attempt to solve problems by exploiting help functionalities of digital learning environments, has been studied across multiple learning environments, little research has been done to study how (and whether) gaming manifests differently across populations of students and learning environments. In this paper, we study the differences in usage of 13 different patterns of actions associated with gaming the system by comparing their distribution across different populations of students using Cognitive Tutor Algebra and across students using one of three learning environments: Cognitive Tutor Algebra, Cognitive Tutor Middle School and ASSISTments. Results suggest that differences in gaming behavior are more strongly associated to the learning environments than to student populations and reveal different trends in how students use fast actions, similar answers and help request in different systems.