Collaborative problem solving (CPS) in virtual environments is an increasingly important context of 21st century learning. However, our understanding of this complex and dynamic phenomenon is still limited. Here, we examine unimodal primitives (activity on the screen, speech, and body movements), and their multimodal combinations during remote CPS. We analyze two datasets where 116 triads collaboratively engaged in a challenging visual programming task using video conferencing software. We investigate how UI-interactions, behavioral primitives, and multimodal patterns were associated with teams' subjective and objective performance outcomes. We found that idling with limited speech (i.e., silence or backchannel feedback only) and without movement was negatively correlated with task performance and with participants' subjective perceptions of the collaboration. However, being silent and focused during solution execution was positively correlated with task performance. Results illustrate that in some cases, multimodal patterns improved the predictions and improved explanatory power over the unimodal primitives. We discuss how the findings can inform the design of real-time interventions for remote CPS.

Confusion has been shown to be prevalent during complex learning and has mixed effects on learning. Whether confusion facilitates or hampers learning may depend on whether it is resolved or not. Confusion resolution, behind which is the resolution of cognitive disequilibrium, requires learners to possess some skills, but it is unclear what these skills are. One possibility may be metacognitive strategies (MS), strategies for regulating cognition. This study examined the relationship between confusion and actions related to MS in Betty's Brain, a computer-based learning environment. The results revealed that MS behavior differed during and outside confusion. However, confusion resolution was not related to MS behavior, and MS did not moderate the effect of confusion on learning.

The use of computer-based systems in classrooms has provided teachers with new opportunities in delivering content to students, supplementing instruction, and assessing student knowledge and comprehension. Among the largest benefits of these systems is their ability to provide students with feedback on their work and also report student performance and progress to their teacher. While computer-based systems can automatically assess student answers to a range of question types, a limitation faced by many systems is in regard to open-ended problems. Many systems are either unable to provide support for open-ended problems, relying on the teacher to grade them manually, or avoid such question types entirely. Due to recent advancements in natural language processing methods, the automation of essay grading has made notable strides. However, much of this research has pertained to domains outside of mathematics, where the use of open-ended problems can be used by teachers to assess students' understanding of mathematical concepts beyond what is possible on other types of problems. This research explores the viability and challenges of developing automated graders of open-ended student responses in mathematics. We further explore how the scale of available data impacts model performance. Focusing on content delivered through the ASSISTments online learning platform, we present a set of analyses pertaining to the development and evaluation of models to predict teacher-assigned grades for student open responses.

Modeling team phenomena from multiparty interactions inherently requires combining signals from multiple teammates, often by weighting strategies. Here, we explored the hypothesis that strategic weighting signals from individual teammates would outperform an equal weighting baseline. Accordingly, we explored role-, trait-, and behavior-based weighting of behavioral signals across team members. We analyzed data from 101 triads engaged in computer-mediated collaborative problem solving (CPS) in an educational physics game. We investigated the accuracy of machine-learned models trained on facial expressions, acoustic-prosodics, eye gaze, and task context information, computed one-minute prior to the end of a game level, at predicting success at solving that level. AUROCs for unimodal models that equally weighted features from the three teammates ranged from .54 to .67, whereas a combination of gaze, face, and task context features, achieved an AUROC of .73. The various multiparty weighting strategies did not outperform an equal-weighting baseline. However, our best nonverbal model (AUROC = .73) outperformed a language-based model (AUROC = .67), and there were some advantages to combining the two (AUROC = .75). Finally, models aimed at prospectively predicting performance on a minute-by-minute basis from the start of the level achieved a lower, but still above-chance, AUROC of .60. We discuss implications for multiparty modeling of team performance and other team constructs. © 2020 ACM.

Vibration plays a significant role in the way users interact with touchscreens. For many users, vibration affords tactile alerts and other enhancements. For eyes-free users and users with visual impairments, vibration can also serve a more primary role in the user interface, such as indicating streets on maps, conveying information about graphs, or even specifying basic graphics. However, vibration is rarely used in current user interfaces beyond basic cuing. Furthermore, designers and developers who do actually use vibration more extensively are often unable to determine the exact properties of the vibration signals they are implementing, due to out-of-the-box software and hardware limitations. We make two contributions in this work. First, we investigate the contextual properties of touchscreen vibrations and how vibrations can be used to effectively convey traditional, embossed elements, such as dashes and dots. To do so, we developed an open source, Android-based library to generate vibrations that are perceptually salient and intuitive, improving upon existing vibration libraries. Second, we conducted a user study with 26 blind or visually impaired users to evaluate and categorize the effects with respect to traditional tactile line profiles. We have established a range of vibration effects that can be reliably generated by our haptic library and are perceptible and distinguishable by users.

It has been shown in multiple studies that expert-created on-demand assistance, such as hint messages, improves student learning in online learning environments. However, there are also evident that certain types of assistance may be detrimental to student learning. In addition, creating and maintaining on-demand assistance are hard and time-consuming. In 2017-2018 academic year, 132,738 distinct problems were assigned inside ASSISTments, but only 38,194 of those problems had on-demand assistance. In order to take on-demand assistance to scale, we needed a system that is able to gather new on-demand assistance and allows us to test and measure its effectiveness. Thus, we designed and deployed TeacherASSIST inside ASSISTments. TeacherASSIST allowed teachers to create on-demand assistance for any problems as they assigned those problems to their students. TeacherASSIST then redistributed on-demand assistance by one teacher to students outside of their classrooms. We found that teachers inside ASSISTments had created 40,292 new instances of assistance for 25,957 different problems in three years. There were 14 teachers who created more than 1,000 instances of on-demand assistance. We also conducted two large-scale randomized controlled experiments to investigate how on-demand assistance created by one teacher affected students outside of their classes. Students who received on-demand assistance for one problem resulted in significant statistical improvement on the next problem performance. The students' improvement in this experiment confirmed our hypothesis that crowd-sourced on-demand assistance was sufficient in quality to improve student learning, allowing us to take on-demand assistance to scale. [This paper was published in: "L@S '20, August 12-14, 2020, Virtual Event, USA," ACM, 2020, pp. 115-124.]

The presence of "big data"in higher education has led to the increasing popularity of predictive analytics for guiding various stakeholders on appropriate actions to support student success. In developing such applications, model selection is a central issue. As such, this study presents a comprehensive examination of five commonly used machine learning models in student success prediction. Using administrative and learning management system (LMS) data for nearly 2,000 college students at a public university, we employ the models to predict short-term and long-term academic success. Beyond the tradeoff between model interpretability and accuracy, we also focus on the fairness of these models with regard to different student populations. Our findings suggest that more interpretable models such as logistic regression do not necessarily compromise predictive accuracy. Also, they lead to no more, if not less, prediction bias against disadvantaged student groups than complicated models. Moreover, prediction biases against certain groups persist even in the fairest model. These results thus recommend using simpler algorithms in conjunction with human evaluation in instructional and institutional applications of student success prediction when valid student features are in place. © 2020 Owner/Author.

With content rapidly moving to the electronic space, access to graphics for individuals with visual impairments is a growing concern. Recent research has demonstrated the potential for representing basic graphical content on touchscreens using vibrations and sounds, yet few guidelines or processes exist to guide the design of multimodal, touchscreen-based graphics. In this work, we seek to address this gap by synergizing our collective research efforts over the past eight years and implementing our findings into a compilation of recommendations, which we validate through an iterative design process and user study. We start by reviewing previous work and then collate findings into a set of design guidelines for generating basic elements of touchscreen-based multimodal graphics. We then use these guidelines to generate exemplary graphics in mathematics, specifically bar charts and geometry concepts. We discuss the iterative design process of moving from guidelines to actual graphics and highlight challenges. We then present a formal user study with 22 participants with visual impairments, comparing learning performance on using touchscreen-rendered graphics to embossed graphics. We conclude with qualitative feedback from participants on the touchscreenbased approach and offer areas of future investigation as these recommendation are expanded to include more complex graphical concepts.

In studies involving human subjects, voluntary participation may lead to sampling bias, thus limiting the generalizability of findings. This effect may be especially pronounced in developmental studies, where parents serve as both the primary environmental input and decision maker of whether their child participates in a study. We present a novel empirical and modeling approach to estimate how parental consent may bias measurements of children’s behavior. Specifically, we coupled naturalistic observations of parent–child interactions in public spaces with a behavioral test with children, and used modeling methods to impute the behavior of children who did not participate. Results showed that parents’ tendency to use questions to teach was associated with both children’s behavior in the test and parents’ tendency to participate. Exploiting these associations with a model-based multiple imputation and a propensity score–matching procedure, we estimated that the means of the participating and not-participating groups could differ as much as 0.23 standard deviations for the test measurements, and standard deviations themselves are likely underestimated. These results suggest that ignoring factors associated with consent may lead to systematic biases when generalizing beyond lab samples, and the proposed general approach provides a way to estimate these biases in future research.

Teacher education research is in still in the process of developing a robust set of tools that can measure the key features of coursework and fieldwork. This article focuses on the design of measures of “opportunities to learn” (OTLs) in content method courses that can be used across large populations of candidates at diverse preparation programs. We draw on the survey and interview data from a broader, longitudinal study of teacher preparation across four programs in the United States (n = 455 candidates). Variance decomposition suggests far greater within-program variation than between-program variation in reported OTL. More nuanced analyses at a single university indicate significant associations between individual characteristics and reports of OTL, suggesting that self-reports of OTL may tell us more about the candidates who complete a survey than the programs that prepare them. Discussion focuses on implications for teacher education research and next steps for studying OTL in methods coursework. © 2019 American Association of Colleges for Teacher Education.