Emerging cad and bim trends in the aec education: An analysis from students\u27 perspective

As the construction industry is moving towards collaborative design and construction practices globally, training the architecture, engineering, and construction (AEC) students professionally related to CAD and BIM became a necessity rather than an option. The advancement in the industry has led to collaborative modelling environments, such as building information modelling (BIM), as an alternative to computer-aided design (CAD) drafting. Educators have shown interest in integrating BIM into the AEC curriculum, where teaching CAD and BIM simultaneously became a challenge due to the differences of two systems. One of the major challenges was to find the appropriate teaching techniques, as educators were unaware of the AEC students’ learning path in CAD and BIM. In order to make sure students learn and benefit from both CAD and BIM, the learning path should be revealed from students’ perspective. This paper summarizes the background and differences of CAD and BIM education, and how the transition from CAD to BIM can be achieved for collaborative working practices. The analysis was performed on freshman and junior level courses to learn the perception of students about CAD and BIM education. A dual-track survey was used to collect responses from AEC students in four consecutive years. The results showed that students prefer BIM to CAD in terms of the friendliness of the user-interface, help functions, and self-detection of mistakes. The survey also revealed that most of the students believed in the need for a BIM specialty course with Construction Management (CM), Structure, and Mechanical-Electrical-Plumbing (MEP) areas. The benefits and challenges of both CAD and BIM-based software from students’ perspectives helps to improve the learning outcomes of CAD/BIM courses to better help students in their learning process, and works as a guideline for educators on how to design and teach CAD/BIM courses simultaneously by considering the learning process and perspectives of students. © 2018 The autho

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

Contemporary developments in teaching and learning introductory programming: Towards a research proposal

The teaching and learning of introductory programming in tertiary institutions is problematic. Failure rates are high and the inability of students to complete small programming tasks at the completion of introductory units is not unusual. The literature on teaching programming contains many examples of changes in teaching strategies and curricula that have been implemented in an effort to reduce failure rates. This paper analyses contemporary research into the area, and summarises developments in the teaching of introductory programming. It also focuses on areas for future research which will potentially lead to improvements in both the teaching and learning of introductory programming. A graphical representation of the issues from the literature that are covered in the document is provided in the introduction

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

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