Nick E. Green

Detecting Life Events in Feeds from Twitter

Short posts on micro-blogs are characterized by high ambiguity and non-standard language. We focus on detecting life events from such micro-blogs, a type of event which have not been paid much attention so far. We discuss the corpus we assembled and our experiments. Simpler models based on unigrams perform better than models that include history, number of retweets and semantic roles.

A Scalable Intelligent Tutoring System Framework for Computer Science Education

Computer Science is a difficult subject with many fundamentals to be taught, usually involving a steep learning curve for many students. It is some of these initial challenges that can turn students away from computer science. We have been developing a new Intelligent Tutoring System, ChiQat-Tutor, that focuses on tutoring of Computer Science fundamentals. Here, we outline the system under development, while bringing particular attention to its architecture and how it attains the primary goals of being easily extensible and providing a low barrier of entry to the end user. The system is broadly broken down into lessons, teaching strategies, and utilities, which work together to promote seamless integration of components. We also cover currently developed components in the form of a case study, as well as detailing our experience of deploying it to an undergraduate Computer Science classroom, leading to learning gains on par with prior work.

Worked Out Examples in Computer Science Tutoring

We annotated and analyzed Worked Out Examples (WOEs) in a corpus of tutoring dialogues on Computer Science data structures. We found that some dialogue moves that occur within WOEs, or se- quences thereof, correlate with learning. Features of WOEs such as length also correlate with learning for some data structures. These re- sults will be used to augment the tutorial tactics available to iList, an ITS that helps student learn linked lists.

Interactions of Individual and Pair Programmers with an Intelligent Tutoring System for Computer Science

Pair programming is a practice where two coders work side by side at one computer. The practice has been linked to many benefits including increased student engagement, satisfaction, and course grades. We present a quantitative study comparing the fine-grained interactions of individual programmers versus pair programmers as they work to solve coding problems using an Intelligent Tutoring System. We collected data from over 115 students resulting in more than 53,000 log events. We discovered that while both individual and pair programmers had equivalent learning gains, pair programmers took significantly less time on most problems, consulted fewer examples, coded more efficiently, and showed more signs of engagement. Individuals adapted to problems requiring new and compounded concepts at a rate similar to pair programmers.

Worked-out Examples in a Computer Science Intelligent Tutoring System

Our CS Intelligent Tutoring System (ITS), ChiQat-Tutor, aims at aiding students in overcoming the initial difficulties in CS education, such as learning data structures. Here, we show our work on utilizing Worked-out Examples (WOE) in our linked list lesson. Despite being a promising strategy, we find that it can be detrimental to student growth.

A Hybrid Model for Teaching Recursion

Novice programmers struggle to understand the concept of recursion, partly because of unfamiliarity with recursive activities, difficulty with visualizing program execution, and difficulty understanding its back flow of control. In this paper we discuss the conceptual and program visualization approaches to teaching recursion. We also introduce our approach to teaching recursion in the ChiQat-Tutor system that relies on ideas from both approaches. ChiQat-Tutor will help Computer Science students learn recursion, develop accurate mental models of recursion, and serve as an effective visualization tool with which hidden contexts of recursion can become evident.

Behavior and Learning of Students Using Worked-Out Examples in a Tutoring System

Worked-out examples have been shown to increase learning gains over problem solving alone. These increases are even greater in novices and those who are learning algorithmic topics, such as those in Computer Science. We have integrated this strategy into our Intelligent Tutoring System and evaluated it on undergraduate students learning the linked list data structure. Although promising, we have identified behavioral differences between high and low gainers - spending less time on an example, and prematurely quitting them led to greater learning.

Integrating Support for Collaboration in a Computer Science Intelligent Tutoring System

Calls for widespread Computer Science CS education have been issued from the White House down and have been met with increased enrollment in CS undergraduate programs. Yet, these programs often suffer from high attrition rates. One successful approach to addressing the problem of low retention has been a focus on group work and collaboration. This paper details the design of a collaborative ITS CIT for foundational CS concepts including basic data structures and algorithms. We investigate the benefit of collaboration to student learning while using the CIT. We compare learning gains of our prior work in a non-collaborative system versus two methods of supporting collaboration in the collaborative-ITS. In our study of 60 students, we found significant learning gains for students using both versions. We also discovered notable differences related to student perception of tutor helpfulness which we will investigate in subsequent work.

A Study of Analogy in Computer Science Tutorial Dialogues

Analogy plays an important role in learning, but its role in teaching Computer Science has hardly been explored. We annotated and analyzed analogy in a corpus of tutoring dialogues on Computer Science data structures. Via linear regression analysis, we established that the presence of analogy and of specific dialogue acts within analogy episodes correlate with learning. We have integrated our findings in our ChiQat-Tutor system, and are currently evaluating the effect of analogy within the system.

Enhancing an intelligent tutoring system to support student collaboration: Effects on learning and behavior

In this study we explore how different methods of structuring collaborative interventions affect student learning and interaction in an Intelligent Tutoring System for Computer Science. We compare two methods of structuring collaboration: one condition, unstructured, does not provide students with feedback on their collaboration; whereas the other condition, semistructured, offers a visualization of group performance over time, partner contribution comparison and feedback, and general tips on collaboration. We present a contrastive analysis of student interaction outcomes between conditions, and explore students reported perceptions of both systems. We found that students in both conditions have significant learning gains, equivalent coding efficiency, and limited reliance on system examples. However, unstructured users are more on-topic in their conversational dialogue, whereas semistructured users exhibit better planning skills as problem difficulty increases.