CS 1181-01: Computer Science II

This is the second course in a two-semester sequence introducing fundamental concepts and techniques for computer science and engineering. The course focuses on problem analysis, advanced programming concepts using JAVA and fundamental data structures. Students learn to analyze problems and evaluate potential solutions with respect to choice of data structures and computational efficiency. Student are exposed to the underlying implementation of basic data structures available in JAVA libraries and develop the skilled needs to extend existing data structures and design new data structures to solve increasingly complex problems. This is an integrated writing course

INTEGRATING COMPUTATIONAL ANALYSIS TECHNIQUES IN EGYPTIAN ACADEMIC ARCHITECTURE CURRICULA

Educational environments must support real life needs, and serve industry demands on both local and worldwide levels. Computer technology can enhance architecture in so many ways. Data Collection, conceptual design, computer aided design, digital model, physical model, virtual reality, simulation, and remote collaboration are different fields that support architecture technically, not to mention the advantages in construction industry. That is why it must be integrated in architecture education, but with care so as not burden creativity. It was found that 75% of the computer modules in architecture departments in Egyptian universities and institutes do not fully integrate with other modules. As an output, two major problems resulted. The first problem is that the design final project comes out either weak or supported by professional paid eligible aid. Secondly, and more important is the need for external software courses to support design projects and not just on the drafting level. Teaching techniques must bear in mind how to integrate modules and courses that aim at enhancing final outputs. As an example is one of the most important courses in the field, which is the computer course taken mostly in early years. Despite the overloaded architecture curricula, because of its multiple identity academia or practice; techniques or aesthetics; science or humanities (Kocaturk, T. and Kiviniemi, A, 2013) how and when to integrate these aspects is crucial. The focus of this paper is to test the importance of integrating computational analysis tools into architecture education (specifically in design). It also aims at emphasizing on the importance of integrating all modules to achieve a qualified output. In order to achieve the fore mentioned goal, a survey on graduates’ educational conditions is first conducted. This is followed by a critical review of some of the existing educational approaches. This paper explores introducing three different computational analysis tools, addressing three different parameters, to evaluate a sample of architecture design projects at a schematic stage. The chosen parameters had a strong impact effect on the design project. Evaluation in a quantitative manner was the third aim of the paper. Quantitative approach was compared to traditional evaluation measures. The focus of this approach indicates a strong necessity to use computer analysis tools during the schematic phase, and recommends suitable building forms and orientations

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

Pedagogical Possibilities for the N-Puzzle Problem

In this paper we present work on a project funded by the National Science Foundation with a goal of unifying the Artificial Intelligence (AI) course around the theme of machine learning. Our work involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Several hands-on laboratory projects that can be closely integrated into an introductory AI course have been developed. We present an overview of one of the projects and describe the associated curricular materials that have been developed. The project uses machine learning as a theme to unify core AI topics in the context of the N-puzzle game. Games provide a rich framework to introduce students to search fundamentals and other core AI concepts. The paper presents several pedagogical possibilities for the N-puzzle game, the rich challenge it offers, and summarizes our experiences using it

Scrum2Kanban: Integrating Kanban and Scrum in a University Software Engineering Capstone Course

Using university capstone courses to teach agile software development methodologies has become commonplace, as agile methods have gained support in professional software development. This usually means students are introduced to and work with the currently most popular agile methodology: Scrum. However, as the agile methods employed in the industry change and are adapted to different contexts, university courses must follow suit. A prime example of this is the Kanban method, which has recently gathered attention in the industry. In this paper, we describe a capstone course design, which adds the hands-on learning of the lean principles advocated by Kanban into a capstone project run with Scrum. This both ensures that students are aware of recent process frameworks and ideas as well as gain a more thorough overview of how agile methods can be employed in practice. We describe the details of the course and analyze the participating students' perceptions as well as our observations. We analyze the development artifacts, created by students during the course in respect to the two different development methodologies. We further present a summary of the lessons learned as well as recommendations for future similar courses. The survey conducted at the end of the course revealed an overwhelmingly positive attitude of students towards the integration of Kanban into the course

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Remote laboratories in teaching and learning – issues impinging on widespread adoption in science and engineering education

This paper discusses the major issues that impinge on the widespread adoption of remote controlled laboratories in science and engineering education. This discussion largely emerges from the work of the PEARL project and is illustrated with examples and evaluation data from the project. Firstly the rationale for wanting to offer students remote experiments is outlined. The paper deliberately avoids discussion of technical implementation issues of remote experiments but instead focuses on issues that impinge on the specification and design of such facilities. This includes pedagogic, usability and accessibility issues. It compares remote experiments to software simulations. It also considers remote experiments in the wider context for educational institutions and outlines issues that will affect their decisions as to whether to adopt this approach. In conclusion it argues that there are significant challenges to be met if remote laboratories are to achieve a widespread presence in education but expresses the hope that this delineation of the issues is a contribution towards meeting these challenges

Using the Internet of Things to Teach Good Software Engineering Practice to High School Students

This paper describes a course to introduce high school students to software engineering in practice using the Internet Of Things (IoT). IoT devices allow students to get quick, visible results without watering down technical aspects of programming and networking. The course has three broad goals: (1) to make software engineering fun and applicable, with the aim of recruiting traditionally underrepresented groups into computing; (2) to make young students begin to approach problems with a design mindset; and (3) to show students that computer science, generally, and software engineering, specifically, is about much more than programming. The course unfolds in three segments. The first is a whirlwind introduction to a subset of IoT technologies. Students complete a specific task (or set of tasks) using each technology. This segment culminates in a “do-it-yourself” project, in which the students implement a simple IoT application using their basic knowledge of the technologies. The course’s second segment introduces software engineering practices, again primarily via hands-on practical tutorials. In the third segment of the course, the students conceive of, design, and implement a project that uses the technologies introduced in the first segment, all while being attentive to the good software engineering practices acquired in the second segment. In addition to presenting the course curriculum, the paper also discusses a first offering of the course in a threeweek summer intensive program in 2017, including assessments done to evaluate the curriculum.Cockrell School of Engineerin

Cross‐curricular IT tools for university students: Developing an effective model

Information technology is now recognized as a key study‐enhancement measure in higher education, and there is increasing demand for the provision of basic IT awareness and skills across the whole range of subject departments. One response to this demand is the central provision of a generic IT course or programme of courses. We draw upon the experience of such courses at the Universities of Glasgow and York to identify some of the significant dimensions in the development and operation of generic IT programmes. These include the policy context, the structure, content and educational stance of the programme, relationship of the programme to existing curricula, and the extent and nature of resourcing, assessment and certification. Operation of such courses raises important issues, such as questions of compulsory IT preparation, study skills, staff development, standardization, institutional policy and evaluation. This discussion is set within current trends in higher education

College of San Mateo Mathematics and Science Teacher Education Program: A Bay Area Collaborative for Excellence in Teacher Preparation with San Jose State University and San Francisco State University

The College of San Mateo (CSM), a community college serving the San Mateo County area of California, is part of a collaborative effort in the San Francisco Bay Area to improve mathematics and science teacher preparation. With funding mainly through the National Science Foundation, the project is locally referred to as the MASTEP Project (Math and Science Teacher Education Program). MASTEP partners include two California State Universities (San Jose State University and San Francisco State University), four community colleges (College of San Mateo, City College of San Francisco, Evergreen Valley Community College, and San Jose City College), selected K-12 schools, and a number of informal educational institutions and local industries. Activities at CSM include recruitment of future math and science teachers through an active future teachers club; tutoring, mentoring and advising through the activities of an integrated science center; and professional development activities and financial support for science and math faculty resulting in their significant involvement in curriculum reform. As a community college, CSM plays a major role in identifying and supporting future teachers and providing these students with courses that are models of effective teaching