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A student's short-term study behavior may not necessary infer his/her long-term behavior. It is very common to see a student changes study strategy throughout a semester and adapts to learning condition. For example, a student may work very hard before the first exam but gradually reducing the effort due to several possible reasons, e.g., being overwhelmed by various course work or discouraged by increasing complexity in the subject. Consistency or differences of one student's behavior is more likely to be discovered by multiple granularity of learning analytics. In this study, we investigate students' study persistence on a self-assessment platform and explore how such a behavioral pattern is related to the performance in exams. A probabilistic mixture model trained by response streams of log data is applied to cluster students' behavior into persistence patterns, which are further categorized into "micro" (short-term) and "macro" (long-term) patterns according to the span of time being modeled. We found four types of micro persistence patterns and several macro patterns in the analysis and analyzed their relations with exam performances. The result suggests that the consistency of persistence patterns can be an important factor driving student's overall performance in the semester, and students achieving higher exam scores show relatively persistent behavior compared to students receiving lower scores.

The contiguity of physical and digital content of embodied learning has been shown to increase student's engagement in educational contexts. Applications with various kinds of physical interactions have been deployed to enhance the learning experiences in many engineering domains. However, even though computer science education (CSE) is one of zestful topics in the recent years, there are few studies focusing on the embodiment of CSE materials, by which the abstract and intangible concepts could be transformed into an intuitive affordance that utilizes sensorimotor experiences during the learning process. We propose an augmented embodiment mobile app designed for computational thinking (CT), specifically the debugging practices and abstraction concept, that makes use of gestures and augmented reality for learners to interact with the content. We examine the logic by the design framework for embodied learning and discuss potential extensions of multimodal analytics in such an application. Our preliminary user study in a middle school shows students' engagement in the application, however, it also reflected several design issues that need to be solved in the next iteration. The future plan of data analysis and experiments is also discussed.

In this work, we propose a new methodology to profile individual students of computer science based on their programming design using a technique called embeddings. We investigate different approaches to analyze user source code submissions in the Python language. We compare the performances of different source code vectorization techniques to predict the correctness of a code submission. In addition, we propose a new mechanism to represent students based on their code submissions for a given set of laboratory tasks on a particular course. This way, we can make deeper recommendations for programming solutions and pathways to support student learning and progression in computer programming modules effectively at a Higher Education Institution. Recent work using Deep Learning tends to work better when more and more data is provided. However, in Learning Analytics, the number of students in a course is an unavoidable limit. Thus we cannot simply generate more data as is done in other domains such as FinTech or Social Network Analysis. Our findings indicate there is a need to learn and develop better mechanisms to extract and learn effective data features from students so as to analyze the students' progression and performance effectively.

There are numerous studies have reported the effectiveness of example-based programming learning. However, less is explored recommending code examples with advanced Machine Learning-based models. In this work, we propose a new method to explore the semantic analytics between programming codes and the annotations. We hypothesize that these semantics analytics will capture mass amount of valuable information that can be used as features to build predictive models. We evaluated the proposed semantic analytics extraction method with multiple deep learning algorithms. Results showed that deep learning models outperformed other models and baseline in most cases. Further analysis indicated that in special cases, the proposed method outperformed deep learning models by restricting false-positive classifications.

It is a common practice for programmers to leave annotations during program development. Most of the annotated documentations are predominantly being used as the archive of the coding events for limited developers. We hypothesize that these annotations captured mass amount of valuable information which can be utilized to identify similar codes or to examine code quality. However, due to the annotating behaviors vary and the language composition can be complex, this work sets out to investigate a systematic method to examine the annotation semantics and their relations with codes. We designed a semantic parser to extract concepts from codes and the corresponding annotations. Additionally, text mining techniques are applied to summarize linguistic features from the annotations. We then build models to predict concepts in programming code annotations. Results show that the proposed semantic modeling method achieved a higher performance compared to a random guessed baseline.

Engaged students are more likely to spend longer time on study, and obtain a better academic performance. Previous studies investigated the role of student engagement in virtual learning environments (e.g., online course, online discussion forum, and intelligent tutoring systems). However, it is still challenging to engage students on a virtual hands-on lab system. Comparing to other virtual learning environment, students have a unique learning model -- learning by doing in virtual hands-on lab. To successfully engage students in a large hands-on lab in cybersecurity education, instructors need to understand how students engage in a lab session, and how their engagement affect lab learning outcome in this specific educational setting. In this paper, we developed a conceptual model, especially for virtual hands-on lab education, to describe student engagement during learning processes in working on virtual hands-on lab tasks. This model adopts two existing educational models on engagement behavior. Preliminary data was collected from 109 students' lab project in a computer network security course at Arizona State University in 2016 Fall semester. Pearson correlation coefficient analysis results reveal two statistically significant preliminary results: the longer time a student spends on reading lab instructional material, the more likely the student works longer time on lab tasks (p

The images' textual descriptions from experts and the general public, subject headings and social tags, are provided to facilitate image search process. Search tactics represents a series of search choices and actions with the textual descriptions provided on the systems during the search process. However, integration of different textual descriptions for image finding have rarely been investigated. This study investigates whether and how two types of different textual descriptions in relation to subject headings and social tags affect user' search tactics. Thirty-six participants were recruited for this study. Multiple methods were employed to collect data, including questionnaires, interviews, and eye-tracking analysis. Eliciting participants' viewing behaviors, this study benefit benefits our understanding of different search tactics of various users and provide adaptive mechanisms to fulfill their search needs. The results of this study provided four guidelines for practitioners when designing adaptive image search interface to fulfill users search tactics.

In this paper, we examine the value of comments in large-scale of discussion forums. We survey the relevant literature and employ the key content quality identification features. We then construct comment semantics features and build several regression models to explore the value of comment semantics dynamics. The results reconfirm the usefulness of several essential quality predictors, including time, reputation, length, and editorship. We also found that comment semantics are valuable to shape the answer quality. Specifically, the diversity of comments significantly contributes to the answer quality. In addition, when searching for good quality answers, it is important to look for global semantics dynamics (diversity), rather than observe local differences (disputable content). Finally, the presence of comments shepherd the community to revise the posts by attracting attentions to the posts and eventually facilitate the editing process.

In this paper, we propose a novel Topic Facet Model (TFM), a probabilistic topic model that assumes all words in single sentence are generated from one topic facet. The model is applied to automatically extract forum posts semantics for uncovering the content latent structures. We further prototype a visual analytics interface to present online discussion forum semantics. We hypothesize that the semantic modeling through analytics on open online discussion forums can help users examine the post content by viewing the summarized topic facets. Our preliminary results demonstrated that TFM can be a promising method to extract topic specificity from conversational and relatively short texts in online programming discussion forums.

Social learning has confirmed its value in enhancing the learning outcomes across a wide spectrum. To support social learning, a visual approach is a common technique to represent and organize multiple students' data in an informative way. This paper presents a design of comparative social visualization for E-learning, which encourages information discovery and social comparisons. Classroom studies confirmed the motivational impact of personalized social guidance provided by the visualization in the target context. The visualization encouraged students to do some work ahead of the course schedule. Moreover, class leaders provided an implicit social guidance for the rest of the class and successfully led the way to discover the most relevant resources creating good trails for the rest of the class. We summarized the evidence of students' engagement and performance through the social visualization interface.

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 21st ACM conference on Hypertext and hypermedia, http://dx.doi.org/10.1145/1810617.1810657, Adaptive link annotation is one of the most popular adaptive educational hypermedia techniques. It has been widely studied and demonstrated its ability to help students to acquire knowledge faster, improve learning outcomes, reduce navigation overhead, increase motivation, and encourage the beneficial non-sequential navigation. However, almost all studies of adaptive link annotation have been performed in the context of dedicated adaptive educational hypermedia systems. The role of this technique in the context of widely popular learning portals has not yet been demonstrated. In this paper, we attempt to fill this gap by investigating the value of adaptive navigation support embedded into the learning portal. We compare the effect of portal-based adaptive navigation support to both the effect of the adaptive navigation support in adaptive educational hypermedia systems and to non-adaptive learning portals., This work is supported by National Science Foundation under Grant IIS-0447083, Spanish Ministry of Science and Education (TIN2007-64718) and the Comunidad Autónoma de Madrid (S2009/TIC-1650)

Problem-tracing questions are popular among teachers of various programming languages. In an assessment mode these questions allows to evaluate student knowledge of language semantics. In a self-assessment mode, they provide an excellent learning tool. A 2004 ITiCSE working group report [4] stressed the importance of this type of questions to build foundation of higher-level knowledge. Yet the use of problem-tracing questions is still limited due to a large authoring overhead. To resolve this bottleneck, we explored the idea of parameterized question generation [2]. We developed QuizPACK [1], a system which can generate parameterized problem-tracing questions for C programming language. We also developed QuizGuide [1], a personalized guidance system for QuizPACK, which models student knowledge and guides students individually to most appropriate questions to try. The results of our studies demonstrated that QuizPACK strongly benefits student knowledge and that QuizGuide personalized guidance technology increased student ability to answer questions correctly and encouraged them to use the system more extensively (which, in turn, positively impacted their knowledge) [1]. However, parameterized questions in area of C programming were not as diverse from the complexity point of view as parameterized questions explored in other areas such as physics [2]. As a result, it was left unclear whether personalized guidance technology can successfully guide students to a broader range of questions from relatively simple to very difficult.The work reported in this poster expands our work on parameterized questions to a more sophisticated domain of object-oriented Java programming, which allowed us to introduce questions of much broader. Capitalizing on our experience with QuizPACK, we developed QuizJET (Java Evaluation Toolkit), which supports authoring, delivery, and evaluation of parameterized questions for Java [3]. We also implemented JavaGuide system (Figure 1), which provides personalized guidance for QuizJET questions. We assessed the impact of adaptive navigation support to student work with questions of different complexity as well as the impact of this technology on weaker and stronger students. The results of two classroom studies indicate that personalized guidance encouraged students to use parameterized questions more extensively and also helped them to access right questions at the right time. Students were 2.5 times more likely to answer a quiz correctly with personalized guidance than without such it. In addition, we found that personalized guidance especially benefited weak students to achieve scores comparable with the scores of strong students on each complexity level of questions.

Each educational resource management website relies on an authoring tool to provide example content. It takes time and experience for authors to create valuable content. Providing support during authoring can affect the quality and quantity of the examples. In this paper we propose a mashup solution to automatically link community wisdom to authors and ease various difficulties in authoring.

This paper analyzes problems encountered by our team while creating an educational digital library of program examples. We present approaches to resolving these problems, and evaluations of the suggested approaches.