Kalina Yacef

Clustering and Sequential Pattern Mining of Online Collaborative Learning Data

Group work is widespread in education. The growing use of online tools supporting group work generates huge amounts of data. We aim to exploit this data to support mirroring: presenting useful high-level views of information about the group, together with desired patterns characterizing the behavior of strong groups. The goal is to enable the groups and their facilitators to see relevant aspects of the groups operation and provide feedback if these are more likely to be associated with positive or negative outcomes and indicate where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. The context is a senior software development project where students use the collaboration tool TRAC. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. The results point to the importance of leadership and group interaction, and give promising indications if they are occurring. Patterns indicating good individual practices were also identified. We found that some key measures can be mined from early data. The results are promising for advising groups at the start and early identification of effective and poor practices, in time for remediation.Group work is widespread in education. The growing use of online tools supporting group work generates huge amounts of data. We aim to exploit this data to support mirroring: presenting useful high...

Collaborative Writing Support Tools on the Cloud

Academic writing, individual or collaborative, is an essential skill for todays graduates. Unfortunately, managing writing activities and providing feedback to students is very labor intensive and academics often opt out of including such learning experiences in their teaching. We describe the architecture for a new collaborative writing support environment used to embed such collaborative learning activities in engineering courses. iWrite provides tools for managing collaborative and individual writing assignments in large cohorts. It outsources the writing tools and the storage of student content to third party cloud-computing vendors (i.e., Google). We further describe how using machine learning and NLP techniques, the architecture provides automated feedback, automatic question generation, and process analysis features.

Who did what? Who said that?: Collaid: an environment for capturing traces of collaborative learning at the tabletop

Tabletops have the potential to provide new ways to support collaborative learning generally and, more specifically, to aid people in learning to collaborate more effectively. To achieve this potential, we need to gain understanding of how to design tabletop environments so that they capture relevant information about collaboration processes so that we can make it available in a form that is useful for learners, their teachers and facilitators. This paper draws upon research in computer supported collaborative learning to establish a set of principles for the design of a tabletop learning system. We then show how these have been used to design our Collaid (Collaborative Learning Aid) environment. Key features of this system are: capture of multi-modal data about collaboration in a tabletop activity using a microphone array and a depth sensor; integration of these data with other parts of the learning system; transforming the data into visualisations depicting the processes that occurred during the collaboration at the table; and sequence mining of the interaction logs. The main contributions of this paper are: our design guidelines to build the Collaid environment and the demonstration of its use in a collaborative concept mapping learning tool applying data mining and visualisations of collaboration.

Recommending people to people: the nature of reciprocal recommenders with a case study in online dating

People-to-people recommenders constitute an important class of recommender systems. Examples include online dating, where people have the common goal of finding a partner, and employment websites where one group of users needs to find a job (employer) and another group needs to find an employee. People-to-people recommenders differ from the traditional items-to-people recommenders as they must satisfy both parties; we call this type of recommender reciprocal. This article is the first to present a comprehensive view of this important recommender class. We first identify the characteristics of reciprocal recommenders and compare them with traditional recommenders, which are widely used in e-commerce websites. We then present a series of studies and evaluations of a content-based reciprocal recommender in the domain of online dating. It uses a large dataset from a major online dating website. We use this case study to illustrate the distinctive requirements of reciprocal recommenders and highlight important challenges, such as the need to avoid bad recommendations since they may make users to feel rejected. Our experiments indicate that, by considering reciprocity, the rate of successful connections can be significantly improved. They also show that, despite the existence of rich explicit profiles, the use of implicit profiles provides more effective recommendations. We conclude with a discussion, linking our work in online dating to the many other domains that require reciprocal recommenders. Our key contributions are the recognition of the reciprocal recommender as an important class of recommender, the identification of its distinctive characteristics and the exploration of how these impact the recommendation process in an extensive case study in the domain of online dating.

CCR: a content-collaborative reciprocal recommender for online dating

We present a new recommender system for online dating. Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people. This analysis provides the foundation for our Content-Collaborative Reciprocal (CCR) recommender approach. The content-based part uses selected user profile features and similarity measure to generate a set of similar users. The collaborative filtering part uses the interactions of the similar users, including the people they like/dislike and are liked/disliked by, to produce reciprocal recommendations. CCR addresses the cold start problem of new users joining the site by being able to provide recommendations immediately, based on their profiles. Evaluation results show that the success rate of the recommendations is 69.26% compared with a baseline of 35.19% for the top 10 ranked recommendations.

Collaborative concept mapping at the tabletop

Concept mapping is a technique where users externalise their conceptual and propositional knowledge of a domain in a way that can be readily understood by others. It is widely used in education, so that a learners understanding is made available to their peers and to teachers. There is considerable potential educational benefit in collaborative concept mapping, and the tabletop is an ideal tool for this. This paper describes Cmate, a tabletop collaborative concept mapping system. We describe its design process and how this draws upon both the principles of concept mapping and on those for creating educational applications on tabletops.

Intelligent teaching assistant systems

Traditionally, intelligent tutoring systems (ITS) are dedicated to learners. They help them learn at their own pace, following a curriculum tailored to their individual needs and receiving individualised feedback. Intelligent teaching assistant systems (ITAs) are dedicated both to learners and teachers. Their aim is to facilitate the whole teaching/learning process by helping the teacher as well as the student. There has been an increasing interest to integrate the teacher as an end-user of the ITS. This paper presents the characteristics and discusses the architecture of ITAs. We illustrate the concept of ITA with two systems that we designed.

Finding someone you will like and who won't reject you

This paper explores ways to address the problem of the high cost problem of poor recommendations in reciprocal recommender systems. These systems recommend one person to another and require that both people like each other for the recommendation to be successful. A notable example, and the focus of our experiments is online dating. In such domains, poor recommendations should be avoided as they cause users to suffer repeated rejection and abandon the site. This paper describes our experiments to create a recommender based on two classes of models: one to predict who each user will like; the other to predict who each user will dislike. We then combine these models to generate recommendations for the user. This work is novel in exploring modelling both peoples likes and dislikes and how to combine these to support a reciprocal recommendation, which is important for many domains, including online dating, employment, mentor-mentee matching and help-helper matching. Using a negative and a positive preference model in a combined manner, we improved the success rate of reciprocal recommendations by 18% while, at the same time, reducing the failure rate by 36% for the top-1 recommendations in comparison to using the positive model of preference alone.

Visualisations for team learning: small teams working on long-term projects

We have developed a set of visualisations mirroring the activity of small teams engaged in a task. These provide a birds-eye view of what is happening in a small team, giving insights into the way that each individual is contributing to the group and the ways that team members interact with each other. We report on our first experience of using these visualisations for a semester-long software development project course. The study revealed that students, especially those with leadership roles, found the visualizations informative and helpful and that over a third of students modified their behaviour accordingly.

Explicit and implicit user preferences in online dating

In this paper we study user behavior in online dating, in particular the differences between the implicit and explicit user preferences. The explicit preferences are stated by the user while the implicit preferences are inferred based on the user behavior on the website. We first show that the explicit preferences are not a good predictor of the success of user interactions. We then propose to learn the implicit preferences from both successful and unsuccessful interactions using a probabilistic machine learning method and show that the learned implicit preferences are a very good predictor of the success of user interactions. We also propose an approach that uses the explicit and implicit preferences to rank the candidates in our recommender system. The results show that the implicit ranking method is significantly more accurate than the explicit and that for a small number of recommendations it is comparable to the performance of the best method that is not based on user preferences.