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

A review of Australasian investigations into problem solving and the novice programmer

This Australasian focused review compares a number of recent studies that have identified difficulties encountered by novices while learning programming and problem solving. These studies have shown that novices are not performing at expected levels and many novices have only a fragile knowledge of programming, which may prevent them from learning and applying problem solving strategies. The review goes on to explore proposals for explicitly incorporating problem solving strategy instruction into introductory programming curricula and assessment, in an attempt to produce improved learning outcomes for novices. Finally, directions suggested by the reviewed studies are gathered and some unanswered questions are raised

Emergent requirements for supporting introductory programming

The problems associated with learning and teaching first year University Computer Science (CS1) programming classes are summarized showing that various support tools and techniques have been developed and evaluated. From this review of applicable support the paper derives ten requirements that a support tool should have in order to improve CS1 student success rate with respect to learning and understanding

New Generation of Educators Initiative: Reform Focus at Comprehensive Grant Sites

This first analysis of the early NGEI work at comprehensive grant campuses shows that collectively campuses are working across points on the pipeline to address the need for teachers who are better prepared to effectively teach to the new standards. While the bulk of the NGEI reform efforts are targeted at teacher preparation program reform, we see NGEI campuses reaching as far back as high school to cultivate early interest in, and preparedness for, teaching in response to local conditions such as limited candidate pools.Within teacher preparation, the early NGEI work of campuses is primarily clustered around the reform of the teacher preparation program coursework and clinical work (reflecting the first and third Key Transformation Elements). Partnerships with districts are at various stages of development and, in several cases, are focused primarily at the school level. A few campuses are reforming the formative feedback process for candidates through their NGEI work (Element 4). Work with district partners on the identification of the key skills, knowledge, and dispositions of well-prepared new teachers (Element 2) and work on continuous improvement based on data on candidates and program completers (Element 5) are less prominent in the NGEI work to date.As campuses clear the hurdle of launching their reforms in the summer and fall and look toward the next phase of NGEI funding, the evaluation (WestEd/SRI) and the facilitation (ConnectEd) teams are poised to provide support to grantees on the Key Transformation Elements that are not yet fully developed across all comprehensive sites, that is:* Partnerships with K–12 district partners to align programming as much as possible.* Shared understandings with K–12 district partners about the key knowledge, skills, and dispositions of a well-prepared new teacher that are used to inform teacher preparation program elements.* Feedback to candidates on their mastery of prioritized skills during preparation.* Data on candidate progress toward mastery of identified knowledge and practices during their training and after program completion.Specifically, ConnectEd is available to assist with implementation coaching and support for comprehensive campus teams and can support the work with K–12 partners.In addition to providing ongoing formative evaluation work across the comprehensive grant sites, the WestEd/SRI team can provide technical support for grantees to assist with the development of high-quality data on candidate progress toward mastery of identified knowledge and practices during their training and after program completion. The data inventories that the evaluation team developed for each campus show that there are opportunities to: a) enhance the quality of existing data, b) improve access to those data, and c) develop new data sources targeted toward the measurement of prioritized skills and knowledge for formative feedback to candidates. In the coming months, the evaluation team will also be seeking opportunities to bridge the system-level work described above in Box 1 with campus efforts to strengthen systems for continuous improvement.

Designing and evaluating the usability of a machine learning API for rapid prototyping music technology

To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable

A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space

We are developing a system for human-robot communication that enables people to communicate with robots in a natural way and is focused on solving problems in a shared space. Our strategy for developing this system is fundamentally data-driven: we use data from multiple input sources and train key components with various machine learning techniques. We developed a web application that is collecting data on how two humans communicate to accomplish a task, as well as a mobile laboratory that is instrumented to collect data on how two humans communicate to accomplish a task in a physically shared space. The data from these systems will be used to train and fine-tune the second stage of our system, in which the robot will be simulated through software. A physical robot will be used in the final stage of our project. We describe these instruments, a test-suite and performance metrics designed to evaluate and automate the data gathering process as well as evaluate an initial data set.Comment: 2017 AAAI Fall Symposium on Natural Communication for Human-Robot Collaboratio