Introducing Java : the case for fundamentals-first

Java has increasingly become the language of choice for teaching introductory programming. In this paper, we examine the different approaches to teaching Java (Objects-first, Fundamentals-first and GUI-first) to ascertain whether there exists an agreed ordering of topics and difficulty levels between nine relatively basic Java topics. The results of our literature survey and student questionnaire suggests that the Fundamentals-first approach may have benefits from the student's point of view and an agreed ordering of the Java topics accompanying this approach has been established

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Design, Implementation, and Evaulation of GIS-Based Learning Materials in an Introductory Geoscience Course

Little is known about how well GIS-based learning lives up to its potential for improving students' skills in problem solving, analysis, and spatial visualization. This article describes a study in which researchers determined ways to quantify student learning that occurred with a GIS-based module on plate tectonics and geologic hazards, and to improve the materials design with the use of classroom observations and field testing. The study found that student difficulties in working with GIS-based activities can be overcome by making some features of the GIS transparent to the user, that a lack of basic geography skills can interfere in the progression of a GIS-based activity, and that some conceptual difficulties can be overcome by providing guiding questions that help students interrogate visual data. In addition, it was noted that some misconceptions in interpretation of two-dimensional maps and three-dimensional block diagrams can persist even after direct instruction. In general, a positive correlation was noted between spatial thinking and GIS-based learning. Educational levels: Graduate or professional

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Interactive Tutorials For Upper Level Quantum Mechanics Courses

This thesis explores the ongoing need for interactive tutorials in the upper level undergraduate Quantum Mechanics course. It first summarizes the development and evaluation of tutorials at the introductory physics level by others, and then challenges the belief that upper level students do not need this type of intervention by citing research in student difficulties in learning Quantum Mechanics. Physics Education research shows that there are common student misconceptions that persist even in the upper level undergraduate courses such as Quantum Mechanics. Cognitive research serves as a guide for effective curriculum design. A description of the iterative process for developing and evaluating the tutorials is discussed. The development and evaluation of "The Time Evolution of a Wave Function" Quantum Interactive Learning Tutorial (QuILT) is described in detail. Finally, the success of the QuILT in reducing the common misconceptions about time evolution is discussed

A collaborative approach to learning programming: a hybrid learning model

The use of cooperative working as a means of developing collaborative skills has been recognised as vital in programming education. This paper presents results obtained from preliminary work to investigate the effectiveness of Pair Programming as a collaborative learning strategy and also its value towards improving programming skills within the laboratory. The potential of Problem Based Learning as a means of further developing cooperative working skills along with problem solving skills is also examined and a hybrid model encompassing both strategies outlined

A Data Science Course for Undergraduates: Thinking with Data

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.Comment: 21 pages total including supplementary material

Threshold concepts and introductory electronics

Electronics and circuit theory are acknowledged as troublesome subjects when first introduced to students. This leads to low student retention into later electronics courses, especially in universities that offer a common first year where students are free to change streams after the first year. We report on a detailed study of the application of Threshold Concept Theory to an introductory electronics course. We identify some Threshold Concepts, explicit and tacit. We postulate that a high density of Threshold Concepts accounts for the reputation for troublesome learning in, and low retention following, these courses. We further suggest that the bimodal distribution of marks that is commonly observed in electronics teaching is a hallmark of a Thresold Concept. This may have significant impact on assessment

Educating programmers: A reflection on barriers to deliberate practice

Copyright @ 2013 HEAProgramming is a craft which often demands that learners engage in a significantly high level of individual practice and experimentation in order to acquire basic competencies. However, practice behaviors can be undermined during the early stages of instruction. This is often the result of seemingly trivial misconceptions that, when left unchecked, create cognitive-affective barriers. These interact with learners' self-beliefs, potentially inducing affective states that inhibit practice. This paper questions how to design a learning environment that can address this issue. It is proposed that analytic and adaptable approaches, which could include soft scaffolding, ongoing detailed formative feedback and a focus on self-enhancement alongside skill development, can help overcome such barriers