Self‐directed Learning Readiness Among Engineering Undergraduate Students

Two studies related to readiness for self‐directed learning of engineering students were performed using the Self‐directed Learning Readiness Scale (SDLRS). A cross‐sectional study of students in the first through final years of study showed that their SDLRS scores are significantly correlated with academic year of study and with grade point average, but not with gender. However, neither academic year of study nor grade point average is a good predictor of SDLRS scores; together they account for less than 5 percent of the observed variance. A second study investigated the effect of a problem‐based learning experience on students' readiness for self‐directed learning. It showed that the average readiness for self‐directed learning increased significantly for students in the problem‐based learning courses. However, investigation of the changes for individual students revealed that only nine of eighteen students showed significant increases in their SDLRS scores, and two showed significant decreases. Potential underlying causes are explored.     

Simulation‐based Learning in Engineering Education: Performance and Transfer in Learning Project Management

This paper reports empirical findings on the impact of keeping and reviewing learning history in a dynamic and interactive simulation environment of engineering education. The simulator for engineering project management had two learning history keeping modes: automatic (simulator‐controlled) and manual (student‐controlled), and a version with no history keeping. A group of industrial engineering students performed four simulation‐runs divided into three identical simple scenarios (single project) and one complicated scenario (multi‐project). The performances of participants running the simulation with the manual history mode were significantly better than users running the simulation with the automatic history mode. Moreover, the effects of using the history mechanism with the ability to undo further enhanced the learning process. The findings imply that students' decision when to record the history during their engineering training process can have a particularly strong enhancing effect on learning. In addition, the simulator as educational innovation improves students learning and performance. The practical implications of using simulators in the field of engineering learning are discussed.     

The Relationships between Students' Conceptions of Learning Engineering and their Preferences for Classroom and Laboratory Learning Environments

This study developed a survey entitled Conceptions of Learning Engineering (CLE), to elicit undergraduate engineering students' conceptions of learning engineering. The reliability and validity of the CLE survey were confirmed through a factor analysis of 321 responses of undergraduate students majoring in electrical engineering. A series of ANOVA analyses revealed that students who preferred a classroom setting tended to conceptualize learning engineering as “testing” and “calculating and practicing,” whereas students who preferred a laboratory setting expressed conceptions of learning engineering as “increasing one's knowledge,” “applying,” “understanding,” and “seeing in a new way.” A further analysis of student essays suggested that learning environments which are student‐centered, peer‐interactive, and teacher‐facilitated help engineering students develop more fruitful conceptions of learning engineering.     

工程制图CAI课件中学习情境的设计

学习情境的设计是课件开发过程的重要组成部分,如何将学习内容融入具体的情境是课件开发的关键。论文介绍了体现真实绘图情境、由形到图、形象直观、仿真学习情境的设计方法。课件开发中以适应学生的认知结构、真实性、选择性、吸引力和整体性为学习情境设计的基本原则,以Powerpoint为平台,应用AutoCAD、Solidworks、Flash和3DSmax等多媒体技术,针对学习中的重点和难点内容,设计不同的学习情境,有利于培养学生的形象思维能力和创新能力。     

Engineering Design Thinking,Teaching, and Learning

This paper is based on the premises that the purpose of engineering education is to graduate engineers who can design, and that design thinking is complex. The paper begins by briefly reviewing the history and role of design in the engineering curriculum. Several dimensions of design thinking are then detailed, explaining why design is hard to learn and harder still to teach, and outlining the research available on how well design thinking skills are learned. The currently most‐favored pedagogical model for teaching design, project‐based learning (PBL), is explored next, along with available assessment data on its success. Two contexts for PBL are emphasized: first‐year cornerstone courses and globally dispersed PBL courses. Finally, the paper lists some of the open research questions that must be answered to identify the best pedagogical practices of improving design learning, after which it closes by making recommendations for research aimed at enhancing design learning.     

Becoming an Engineer: Toward a Three Dimensional View of Engineering Learning

In this paper we develop an analytical framework we refer to as “Becoming an Engineer” that focuses upon changes occurring over time as students traverse their undergraduate educations in engineering. This analytical framework involves three related dimensions that we track over time: disciplinary knowledge, identification, and navigation. Our analysis illustrates how these three dimensions enable us to understand how students become, or do not become, engineers by examining how these three interrelated dimensions unfold over time. This study is based on longitudinal ethnographic data from which we have developed “person‐centered ethnographies” focused on individual students' pathways through engineering. We present comparative analysis, spanning four schools and four years. We also present person‐centered ethnographic case studies that illustrate how our conceptual dimensions interrelate. Our discussion draws some educational implications from our analysis and proposes further lines of research.     

The Effect of Social Presence on Affective and Cognitive Learning in an International Engineering Course Taught via Distance Learning

Background Distance learning course formats can alter modes of information exchange and interpersonal interaction relative to traditional course formats. Purpose (Hypothesis ) To determine the effect of a distance course format on the knowledge acquisition (cognitive learning) and satisfaction (affective learning) of students, we investigated student learning responses and social presence during a graduate‐level engineering course taught via traditional (i.e., professor present in the classroom) and synchronous distance‐learning formats. Design /Method Direct quantification of participation, academic performance assessment based on homework and exam scores, and survey‐based assessments of student perceptions of the course were collected. Based on these data, cognitive and affective learning responses to different technological and interaction‐based aspects of the course were determined for each course format. Results We show that while affective learning decreased for students in the distance format course relative to the traditional format, cognitive learning was comparable. Our results suggest that loss of satellite connection and audio losses had a stronger negative effect on student perceptions than video disturbances, and that participation was the most important factor influencing affective learning. Conclusions While our findings do not suggest that cognitive learning is strongly affected by social presence, implementing strategies to enhance social presence may improve the overall learning experience and make distance learning more enjoyable for students.     

基于认知过程的机类基础课组合式学习

高职院校机类技术基础课担负着从基础课过渡到专业课的承上启下重任.高职教育必须为学生可持续发展打好基础.应用建构主义理论,分析此类课程特性、课程知识建构特征,指出“技能”的含义不是“动作”,尽管技术基础课不主授专业技能,但课程知识都支撑专业技能的底座.阐述了推进组合式学习的理念、特征和策略.     

Machine Learning in Chemical Engineering: Strengths,Weaknesses, Opportunities,and Threats

Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases, benchmarks, and representations for chemical applications and new machine learning frameworks. Machine learning has significant advantages over traditional modeling techniques, including flexibility, accuracy, and execution speed. These strengths also come with weaknesses, such as the lack of interpretability of these black-box models. The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns, learn from data, and become more intelligent over time. The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis. Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.     

Investigating the Effect of 3D Simulation Based Learning on the Motivation and Performance of Engineering Students

Background Simulation‐based Learning (SBL) was used in Machining Technology, a sixty‐hour module for second year engineering students, at the School of Engineering at Temasek Polytechnic. The aim of this study was to investigate the effect of SBL on learners' motivation and performance. In assessing students' motivation, we adopted a framework based on the Self‐determination Theory (SDT), chosen on account of its comprehensive treatment of the relationship between students' perceived needs satisfaction and their motivation. Purpose (Hypothesis ) It is hypothesized that SBL, which provides learners with interactive learning experiences, will enhance students' motivation and performance. We explored the effect of SBL on students' perceived psychological needs satisfaction, motivation, and learning, and how SBL affected students' understanding and application of content knowledge. Design /Method The intervention procedure involved the incorporation of SBL in Machining Technology, a 60 hour module in the mechanical engineering program. Survey findings and post‐intervention assessment outcomes were used to assess the students' perceptions of their basic psychological needs satisfaction, motivation, and performance. Results Our findings suggest that the students perceived their psychological needs to be satisfied and had high levels of self‐determined motivation. Students who undertook SBL had higher mean performance test scores, although SBL may have differential effects on learners depending on factors such as gender, educational backgrounds, and IT knowledge. Conclusions Our findings suggest that the students perceived their basic psychological needs to be met and that SBL can potentially enhance self‐determined motivation as well as improve learning in general.     

Multi-Label Learning Based on Transfer Learning and Label Correlation

In recent years, multi-label learning has received a lot of attention. However, most of the existing methods only consider global label correlation or local label correlation. In fact, on the one hand, both global and local label correlations can appear in real-world situation at same time. On the other hand, we should not be limited to pairwise labels while ignoring the high-order label correlation. In this paper, we propose a novel and effective method called GLLCBN for multi-label learning. Firstly, we obtain the global label correlation by exploiting label semantic similarity. Then, we analyze the pairwise labels in the label space of the data set to acquire the local correlation. Next, we build the original version of the label dependency model by global and local label correlations. After that, we use graph theory, probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model, so as to get the optimal label dependent model. Finally, we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label learning. The experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.     

Quasi-Phase Equilibrium Prediction of Multi-Element Alloys Based on Machine Learning and Deep Learning

In this study, a phase field model is established to simulate the microstructure formation during the solidification of dendrites by taking the Al-Cu-Mg ternary alloy as an example, and machine learning and deep learning methods are combined with the Kim-Kim-Suzuki (KKS) phase field model to predict the quasi-phase equilibrium. The paper first uses the least squares method to obtain the required data and then applies eight machine learning methods and five deep learning methods to train the quasi-phase equilibrium prediction models. After obtaining different models, this paper compares the reliability of the established models by using the test data and uses two evaluation criteria to analyze the performance of these models. This work find that the performance of the established deep learning models is generally better than that of the machine learning models, and the Multilayer Perceptron (MLP) based quasi-phase equilibrium prediction model achieves the best performance. Meanwhile the Convolutional Neural Network (CNN) based model also achieves competitive results. The experimental results show that the model proposed in this paper can predict the quasi-phase equilibrium of the KKS phase-field model accurately, which proves that it is feasible to combine machine learning and deep learning methods with phase-field model simulation.