Niko Myller

Visualizing programs with Jeliot 3

We present a program visualization tool called Jeliot 3 that is designed to aid novice students to learn procedural and object oriented programming. The key feature of Jeliot is the fully or semi-automatic visualization of the data and control flows. The development process of Jeliot has been research-oriented, meaning that all the different versions have had their own research agenda rising from the design of the previous version and their empirical evaluations. In this process, the user interface and visualization has evolved to better suit the targeted audience, which in the case of Jeliot 3, is novice programmers. In this paper we explain the model for the system and introduce the features of the user interface and visualization engine. Moreover, we have developed an intermediate language that is used to decouple the interpretation of the program from its visualization. This has led to a modular design that permits both internal and external extensibility.

Perspectives on Program Animation with Jeliot

The Jeliot family consists of three program animation environments which are based on a self-animation paradigm. A student can visualize her Java code without inserting additional calls to animation primitives. The design of the animation environments has been guided by the analysis of feedback from high school and university students. Evaluation studies indicate the benefit of dedicated animation environments for different user groups like novice programmers. Based on the results of these studies, we present plans for a future work on Jeliot.

Extending the Engagement Taxonomy: Software Visualization and Collaborative Learning

As collaborative learning in general, and pair programming in particular, has become widely adopted in computer science education, so has the use of pedagogical visualization tools for facilitating collaboration. However, there is little theory on collaborative learning with visualization, and few studies on their effect on each other. We build on the concept of the engagement taxonomy and extend it to classify finer variations in the engagement that result from the use of a visualization tool. We analyze the applicability of the taxonomy to the description of the differences in the collaboration process when visualization is used. Our hypothesis is that increasing the level of engagement between learners and the visualization tool results in a higher positive impact of the visualization on the collaboration process. This article describes an empirical investigation designed to test the hypothesis. The results provide support for our extended engagement taxonomy and hypothesis by showing that the collaborative activities of the students and the engagement levels are correlated.

A decade of research and development on program animation: The Jeliot experience☆

Abstract Jeliot is a program animation system for teaching and learning elementary programming that has been developed over the past decade, building on the Eliot animation system developed several years before. Extensive pedagogical research has been done on various aspects of the use of Jeliot including improvements in learning, effects on attention, and acceptance by teachers. This paper surveys this research and development, and summarizes the experience and the lessons learned.

Automatic Essay Grading with Probabilistic Latent Semantic Analysis

Probabilistic Latent Semantic Analysis (PLSA) is an information retrieval technique proposed to improve the problems found in Latent Semantic Analysis (LSA). We have applied both LSA and PLSA in our system for grading essays written in Finnish, called Automatic Essay Assessor (AEA). We report the results comparing PLSA and LSA with three essay sets from various subjects. The methods were found to be almost equal in the accuracy measured by Spearman correlation between the grades given by the system and a human. Furthermore, we propose methods for improving the usage of PLSA in essay grading.

Characterizing research in computing education: a preliminary analysis of the literature

This paper presents a preliminary analysis of research papers in computing education. While previous analysis has explored what research is being done in computing education, this project explores how that research is being done. We present our classification system, then the results of applying it to the papers from all five years of ICER. We find that this subset of computing education research has more in common with research in information systems than with that in computer science or software engineering; and that the papers published at ICER generally appear to conform to the specified ICER requirements.

Automatic Generation of Prediction Questions during Program Visualization

Based on previous research, it seems that the activities performed by and the engagement of the students matter more than the content of the visualization. One way to engage students to interact with a visualization is to present them with prediction questions. This has been shown to be beneficial for learning. Based on the engagement taxonomy and benefits of the question answering during the algorithm visualization, we propose to implement an automatic question generation into a program visualization tool, Jeliot 3. In this paper, we explain how the automatic question generation can be incorporated into the current design of Jeliot 3. In addition, we provide various example questions that could be automatically generated based on the data obtained during the visualization process.

Theoretical underpinnings of computing education research: what is the evidence?

We analyze the Computing Education Research (CER) literature to discover what theories, conceptual models and frameworks recent CER builds on. This gives rise to a broad understanding of the theoretical basis of CER that is useful for researchers working in that area, and has the potential to help CER develop its own identity as an independent field of study. Our analysis takes in seven years of publications (2005-2011, 308 papers) in three venues that publish long research papers in computing education: the journals ACM Transactions of Computing Education (TOCE) and Computer Science Education (CSEd), and the conference International Computing Education Research Workshop (ICER). We looked at the theoretical background works that are used or extended in the papers, not just referred to when describing related work. These background works include theories, conceptual models and frameworks. For each background work we tried to identify the discipline from which it originates, to gain an understanding of how CER relates to its neighboring fields. We also identified theoretical works originating within CER itself, showing that the field is building on its own theoretical works. Our main findings are that there is a great richness of work on which recent CER papers build; there are no prevailing theoretical or technical works that are broadly applied across CER; about half the analyzed papers build on no previous theoretical work, but a considerable share of these are building their own theoretical constructions. We discuss the significance of these findings for the whole field and conclude with some recommendations.

Applying latent dirichlet allocation to automatic essay grading

We report experiments on automatic essay grading using Latent Dirichlet Allocation (LDA). LDA is a “bag-of-words” type of language modeling and dimension reduction method, reported to outperform other related methods, Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (PLSA) in Information Retrieval (IR) domain. We introduce LDA in detail and compare its strengths and weaknesses to LSA and PLSA. We also compare empirically the performance of LDA to LSA and PLSA. The experiments were run with three essay sets consisting in total of 283 essays from different domains. On contrary to the findings in IR, LDA achieved slightly worse results compared to LSA and PLSA in the experiments. We state the reasons for LSA and PLSA outperforming LDA and indicate further research directions.

Using data mining for improving web-based course design

The distance education field offers several potential data sources for data mining applications. These applications can help both instructors and students in the web-based learning setting. One of the interesting aspects of data mining in education is to collect and interpret the information from several courses. This information can be used for example to assign heterogeneous groups in programming courses or projects and to evaluate the actual learning.