Roya Hosseini

Mastery grids: an open-source social educational progress visualization

Many pieces of educational software are underused by students. Open learning model and social visualization are two approaches which have been helpful in ameliorating that low usage problem. This article introduces a fusion of these two ideas in a form of social progress visualization. A classroom evaluation indicates that this combination may be effective in engaging students, guiding them to suitable content, and enabling faster content access.

An Intelligent Interface for Learning Content: Combining an Open Learner Model and Social Comparison to Support Self-Regulated Learning and Engagement

We present the Mastery Grids system, an intelligent interface for online learning content that combines open learner modeling (OLM) and social comparison features. We grounded the design of Mastery Grids in self-regulated learning and learning motivation theories, as well as in our past work in social comparison, OLM, and adaptive navigation support. The force behind the interface is the combination of adaptive navigation functionality with the mastery-oriented aspects of OLM and the performance-oriented aspects of social comparison. We examined different configurations of Mastery Grids in two classroom studies and report the results of analysis of log data and survey responses. The results show how Mastery Grids interacts with different factors, like gender and achievement-goal orientation, and ultimately, its impact on student engagement, performance, and motivation.

Mastery Grids: An Open Source Social Educational Progress Visualization

While many pieces of educational software used in the classroom have been found to positively affect learning, they often are underused by students. Open learning model and social visualization are two approaches which have been helpful in ameliorating that low usage problem. This article introduces a fusion of these two ideas in a form of social progress visualization. A classroom evaluation indicates that this combination may be effective in engaging students, guiding them to suitable content, and enabling faster content access.

Open Social Student Modeling for Personalized Learning

Open student modeling (OSM) is an approach to technology-based learning, which makes student models available to the learners for exploration. OSM is known for its ability to increase student engagement, motivation, and knowledge reflection. A recent extension of OSM known as open social student modeling (OSSM) complements cognitive aspects of OSM with social aspects by allowing students to explore models of peer students and/or an aggregated class model. In this paper, we introduce an OSSM interface, MasteryGrids, and report the results of a large-scale classroom study, which explored the impact of the social dimension of OSSM. Students in a database management course accessed nonrequired learning materials (examples and problems) via the MasteryGrids interface using either OSM or OSSM. The results revealed that OSSM-enhanced learning, especially for students with lower prior knowledge, compared with OSM. It also enhanced user attitude and engagement. Amount of student usage, efficiency of student usage, and student attitude varied depending on the combination of interface condition (OSM/OSSM), gender, and student social comparison orientation.

The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling

Open Student Modeling (OSM) is a popular technology that makes traditionally hidden student models available to the learners for exploration. OSM is known for its ability to increase student engagement, motivation, and knowledge reflection. A recent extension of OSM known as Open Social Student Modeling (OSSM) attempts to enhance cognitive aspects of OSM with social aspects by allowing students to explore models of peer students or the whole class. In this paper, we introduce MasteryGrids, a scalable OSSM interface and report the results of a large-scale classroom study that explored the value of adding social dimension to OSM. The results of the study reveal a remarkable engaging potential of OSSM as well as its impact on learning effectiveness and user attitude.

What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling

One of the original goals of intelligent educational systems was to guide each student to the most appropriate educational content. In previous studies, we explored both knowledge-based and social guidance approaches and learned that each has a weak side. In the present work, we have explored the idea of combining social guidance with more traditional knowledge-based guidance systems in hopes of supporting more optimal content navigation. We propose a greedy sequencing approach aimed at maximizing each student’s level of knowledge and implemented it in the context of an open social student modeling interface. We performed a classroom study to examine the impact of this combined guidance approach. The results of our classroom study show that a greedy guidance approach positively affected students’ navigation, increased the speed of learning for strong students, and improved the overall performance of students, both within the system and through end-of-course assessments.

KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model

This paper presents our attempt to develop a personalized exam preparation tool for Java/OOP classes based on a fine-grained concept model of Java knowledge. Our goal was to explore two most popular student model-based approaches: open student modeling and problem sequencing. The result of our work is a Java exam preparation tool, Knowledge Zoom. The tool combines an open concept-level student model component, Knowledge Explorer and a concept-based sequencing component, Knowledge Maximizer into a single interface. This paper presents both components of Knowledge Zoom, reports results of its evaluation, and discusses lessons learned.

Knowledge Maximizer: Concept-Based Adaptive Problem Sequencing for Exam Preparation

To support introductory Java programming students in preparing for their exams, we developed Knowledge Maximizer as a concept-based problem sequencing tool that considers a fine-grained concept-level model of student knowledge accumulated over the semester and attempts to bridge the possible knowledge gaps in the most efficient way. This paper presents the sequencing approach behind the Knowledge Maximizer and its classroom evaluation.

Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses

Stereotypes are frequently used in real life to classify students according to their performance in class. In literature, we can find many references to weaker students, fast learners, struggling students, etc. Given the lack of detailed data about students, these or other kinds of stereotypes could be potentially used for user modeling and personalization in the educational context. Recent research in MOOC context demonstrated that data-driven learner stereotypes could work well for detecting and preventing student dropouts. In this paper, we are exploring the application of stereotype-based modeling to a more challenging task -- predicting student problem-solving and learning in two programming courses and two MOOCs. We explore traditional stereotypes based on readily available factors like gender or education level as well as some advanced data-driven approaches to group students based on their problem-solving behavior. Each of the approaches to form student stereotype cohorts is validated by comparing models of student learning: do students in different groups learn differently? In the search for the stereotypes that could be used for adaptation, the paper examines ten approaches. We compare the performance of these approaches and draw conclusions for future research.

A study of concept-based similarity approaches for recommending program examples

ABSTRACT This paper investigates a range of concept-based example recommendation approaches that we developed to provide example-based problem-solving support in the domain of programming. The goal of these approaches is to offer students a set of most relevant remedial examples when they have trouble solving a code comprehension problem where students examine a program code to determine its output or the final value of a variable. In this paper, we use the ideas of semantic-level similarity-based linking developed in the area of intelligent hypertext to generate examples for the given problem. To determine the best-performing approach, we explored two groups of similarity approaches for selecting examples: non-structural approaches focusing on examples that are similar to the problem in terms of concept coverage and structural approaches focusing on examples that are similar to the problem by the structure of the content. We also explored the value of personalized example recommendation based on students knowledge levels and learning goal of the exercise. The paper presents concept-based similarity approaches that we developed, explains the data collection studies and reports the result of comparative analysis. The results of our analysis showed better ranking performance of the personalized structural variant of cosine similarity approach.