Jacqueline L. Whalley

Not seeing the forest for the trees: novice programmers and the SOLO taxonomy

This paper reports on the authors use of the SOLO taxonomy to describe differences in the way students and educators solve small code reading exercises. SOLO is a general educational taxonomy, and has not previously been applied to the study of how novice programmers manifest their understanding of code. Data was collected in the form of written and think-aloud responses from students (novices) and educators (experts), using exam questions. During analysis, the responses were mapped to the different levels of the SOLO taxonomy. From think-aloud responses, the authors found that educators tended to manifest a SOLO relational response on small reading problems, whereas students tended to manifest a multistructural response. These results are consistent with the literature on the psychology of programming, but the work in this paper extends on these findings by analyzing the design of exam questions.

GenGIS: A geospatial information system for genomic data

The increasing availability of genetic sequence data associated with explicit geographic and ecological information is offering new opportunities to study the processes that shape biodiversity. The generation and testing of hypotheses using these data sets requires effective tools for mathematical and visual analysis that can integrate digital maps, ecological data, and large genetic, genomic, or metagenomic data sets. GenGIS is a free and open-source software package that supports the integration of digital map data with genetic sequences and environmental information from multiple sample sites. Essential bioinformatic and statistical tools are integrated into the software, allowing the user a wide range of analysis options for their sequence data. Data visualizations are combined with the cartographic display to yield a clear view of the relationship between geography and genomic diversity, with a particular focus on the hierarchical clustering of sites based on their similarity or phylogenetic proximity. Here we outline the features of GenGIS and demonstrate its application to georeferenced microbial metagenomic, HIV-1, and human mitochondrial DNA data sets.

Multilayer hybrid visualizations to support 3D GIS

Abstract In this paper, we present a unique hybrid visualization system for spatial data. Although some existing 3D GIS systems offer 2D views they are typically isolated from the 3D view in that they are presented in a separate window. Our system is a novel hybrid 2D/3D approach that seamlessly integrates 2D and 3D views of the same data. In our interface, multiple layers of information are continuously transformed between the 2D and 3D modes under the control of the user, directly over a base terrain. In this way, our prototype system is able to depict 2D and 3D views within the same window. This has advantages, since 2D and 3D visualizations can each be easier to interpret in different contexts. In this work we develop this concept of a hybrid visualization by presenting a comprehensive set of capabilities within our distinctive system. These include new facilities such as: hybrid landmark, 3D point, and chart layers, the grouping of multiple hybrid layers, layer painting, the merging of layer controls and consistent zooming functionality.

A 2D/3D hybrid geographical information system

We present a unique Geographical Information System (GIS) that seamlessly integrates 2D and 3D views of the same spatial and aspatial data. Multiple layers of information are continuously transformed between the 2D and 3D modes under the control of the user, directly over a base-terrain.Although 2D/3D hybrid (or combination) displays are now widely used in medical applications such as tomography, they have not been explored to any great extent in GIS applications. And although many existing commercial 3D GIS systems have 2D views they are typically isolated from the 3D view in that they are presented in a separate window. In this paper we introduce a 2D/3D hybrid GIS that allows the user to view the 2D data in direct relation to the 3D view within the same view.

A two phase method for general audio segmentation

This paper presents a model-free and training-free twophase method for audio segmentation that separates monophonic heterogeneous audio files into acoustically homogeneous regions where each region contains a single sound. A rough segmentation separates audio input into audio clips based on silence detection in the time domain. Then a self-similarity matrix, based on selected audio features in the frequency domain to discover the level of similarity between frames in the audio clip, is calculated. Subsequently an edge detection method is used to find regions in the similarity image that determine plausible sounds in the audio clip. The results of the two phases are combined to form the final boundaries for the input audio. This two-phase method is evaluated using established methods and a standard non-musical database. The method reported here offers more accurate segmentation results than existing methods for audio segmentation. We propose that this approach could be adapted as an efficient preprocessing stage in other audio processing systems such as audio retrieval, classification, music analysis and summarization.

The synthesis of 2-substituted isoflavones for immunoassay.

Details are given of the preparation of isoflavones derivatised so that they act as haptens in the development of immunoassays for large scale screening for phytoestrogens in biological fluid. A specifically designed new cyclisation reaction allowed the direct conversion of 2-acyloxydeoxybenzoins to the required isoflavones.

Student values and interests in capstone project selection

In this paper, we present an exploration of the values which students consider when selecting their capstone project. The five core values considered were difficulty, experience gained, social value, fun, and topic familiarity. Students were surveyed and asked to indicate the level of importance they placed on each of these core values. Difficulty and experience gained were found to be the values considered most important. Students who placed more emphasis on fun and very little on difficulty were found to be more likely to perform poorly on the project. Further work is suggested to refine the survey instrument used and to investigate the reasons behind student priorities.

Developing Assessments to Determine Mastery of Programming Fundamentals

Current CS1 learning outcomes are relatively general, specifying tasks such as designing, implementing, testing and debugging programs that use some fundamental programming constructs. These outcomes impact what we teach, our expectations, and our assessments. Although prior work has demonstrated the utility of single concept assessments, most assessments used in formal examinations combine numerous heterogeneous concepts, resulting in complex and difficult tasks. The exclusive use of these traditional assessments results in two major problems. Firstly, teachers who are administering the assessments find it difficult to quantify what students are struggling with. A program that fails to compile or fails to pass test cases provides little information about what a student can successfully achieve. Secondly, it deprives the students of feedback about what they do know and what they can achieve, which we know is one of the most significant factors that influence student learning. Our traditional compound assessment tasks limit the nature and quality of feedback to teachers and students alike, and may contribute to the perceived difficulty and high drop out rates observed in introductory programming courses. This working group aims to decompose existing CS1 learning outcomes into their component parts, and develop assessment items focused on these individual components. We aim to create exemplar assessments that may be used for formative or summative feedback on student understanding of specific components of programming knowledge. Focusing on independent components of programming increases opportunities for students to demonstrate what they can achieve, and may improve diagnosis of student difficulties. It is likely that this project would be of particular relevance to teachers and researchers interested in mastery learning, or those who wish to demonstrate tight coupling between learning outcomes and assessment tasks. It may also be of relevance to teachers and researchers interested in promoting a more positive mindset in the classroom, focusing on what students do know and what they can achieve during a typical CS1 course.

Particulate Matter Sampling Techniques and Data Modelling Methods

Particulate matter with 10 μm or less in diameter (PM10) is known to have adverse effects on human health and the environment. For countries committed to reducing PM10 emissions, it is essential to have models that accurately estimate and predict PM10 concentrations for reporting and monitoring purposes. In this chapter, a broad overview of recent empirical statistical and machine learning techniques for modelling PM10 is presented. This includes the instrumentation used to measure particulate matter, data preprocessing, the selection of explanatory variables and modelling methods. Key features of some PM10 prediction models developed in the last 10 years are described, and current work modelling and predicting PM10 trends in New Zealand—a remote country of islands in the South Pacific Ocean—are examined. In conclusion, the issues and challenges faced when modelling PM10 are discussed and suggestions for future avenues of investigation, which could improve the precision of PM10 prediction and estimation models are presented.

Audio classification based on adaptive partitioning

This paper presents an audio classification system that provides improved accuracy, robustness and flexibility over reported content-based audio classification methods. The system reads an input audio file, performs segmentation and classification of the composite sounds contained within the file and, for each sound clip, determines the most plausible matching class of audio in the database. Improvements in the accuracy of audio classification are largely due to the partitioning of the input audio file into homogeneous segments while the incorporation of new class detection offers greater flexibility of use.