The power of feedback.

1. Word frequency and vocabulary size Paul Nation and Rob Waring 2. Variation in spoken and written language Michael McCarthy and Ron Carter 3. Vocabulary connections Rosamund Moon 4. Vocabulary and context William Nagy 5. Productive vs. receptive aspects of vocabulary Francine Melka 6. Models of lexical acquisition Paul Meara 7. Cognitive constraints on vocabulary acquisition Nick Ellis 8. Intralexical factors affecting the difficulty of vocabulary acquisition Batia Laufer 9. L1 influences on L2 vocabulary acquisition 10. Acquisition of the formal aspects of vocabulary Michael Swan 11. Vocabulary learning strategies Ann Ryan 12. Vocabulary and the syllabus Norbert Schmitt 13. Teaching vocabulary: most recent trends Felicity O'Dell 14. Vocabulary and testing John Read 15. Dictionaries, thesauruses, lexical resources Phil Schofield.

詞彙:描述、習得與教學=Vocabulary : description, acquisition and pedagogy

Review of drop impact on heated walls

Review of spray cooling – Part 1: Single-phase and nucleate boiling regimes, and critical heat flux

https://terpconnect.umd.edu/~kimjh/Documents%20and%20movies/IJHMT%20spray%20copy.pdf

Single nozzle spray cooling heat transfer mechanisms

The goal of engineering examinations is to measure the academic performance of our students with respect to learning outcomes. On exams, the students are often asked contextualized questions using vocabulary that might be unfamiliar. A study is being conducted that investigates the accessibility of language on engineering exams with the goal of making language clearer for all students.Specifically, a Term-Frequency Inverse-Document Frequency (TF-IDF) algorithm is used to characterize words on a given engineering exam. By comparing data across different test cases, the TFIDF algorithm appears to accurately distinguish discipline-specific vocabulary from non-disciplinespecific vocabulary. These results inform an approach that could maintain the integrity of engineering exams and create more accessible assessment tools.

https://ojs.library.queensu.ca/index.php/PCEEA/article/download/4684/4666

Identifying discipline-specific vocabulary on engineering exams

Understanding technical vocabulary is often a desired learning outcome in engineering education, and a significant part of professional communication in the engineering profession. Language used in engineering education plays a key role in creating an accessible and inclusive learning environment. The corpus of language common to both the instructor and student ought to converge as the student masters the course content. Instructors may currently use techniques to help identify this vocabulary, including referring to glossaries and increasing the frequency of their use in the classroom. There is an opportunity to increase transparency and accessibility to such vocabulary by developing an automated software-based tool that can be used by instructors to create customized course-specific wordlists for their courses. Using text extracted from instructional material in a course, the algorithm developed for this study is able to hierarchically identify and display course-specific terminology using principles from artificial intelligence, linguistics, higher education, and industrial engineering. Grounded in the theory of Universal Instructional Design, these wordlists can be integrated into a syllabus and then be used as a teaching aid to promote an accessible engineering education. The goal is to reduce barriers to learning by developing an explicitly-identified and robust list of vocabulary for all students in a given course. Creating an automated program that improves vocabulary information over time keeps it relevant and usable by instructors as well as students. Presently, there is no automated method to develop course-specific vocabulary lists. To fill this gap, the authors have created a computer program, using a repository of over 2200 engineering exams since the year 2000 from the University of Toronto, which automatically identifies domain-specific terms on any given engineering exam. Specifically, each word from each exam is digitized and computed against others using a modified form of the Term-Frequency Inverse Document-Frequency (TF-IDF) algorithm to generate lists of context-specific characteristic terms. This well-known algorithm is used in the field of computational linguistics as a method of identifying words characteristic to a document, given a comparator set of documents. In this work, a modified approach has been developed that uses several comparator sets to produce a list of engineering vocabulary for a course. The effectiveness of this approach is evaluated by comparing the results to the judgment of subject-matter experts. This paper will use the data gathered to discuss the efficacy of this automated program in the context of engineering research methods, and will identify ways in which to make this program accessible to, and usable by, more educators in the field of engineering education.

/pdf/engineering-vocabulary-development-using-an-automated-458bacaequ.pdf

Engineering Vocabulary Development Using an Automated Software Tool

Freeform comments as a means of providing formative feedback on engineering problems, from the perspective of feedback providers (i.e. assessors), is examined. The aim of this research is to collect and analyze assessors’ ratings on the usability of this type of task. Two course topics with different error loads in the sample solutions were used as the basis for the work. Assessors were divided into two groups: Group 1 received an evaluation package containing first year mechanics students’ test solutions with a high error load (Error1=32), while Group 2 received first year circuits students’ test solutions with low error load (Error2=11). Assessment time was held constant (ttot=20min). A standard instrument for usability was utilized. Analysis of the survey data from the assessors (n1=11, n2=19) revealed some significant differences between the two groups. In particular, Group 1 reported a lower degree of perceived consistency in marking relative to Group 2.

/pdf/usability-analysis-of-freeform-marking-on-engineering-xzgurriang.pdf

Usability Analysis of Freeform Marking on Engineering Problem Solving

In our work, we are focusing on the use of classroom observation to provide feedback on instructional space design. An initiative to redesign teaching space began a decade ago at a large, research-intensive institution. In September 2018, a large-scale (477 seat) active learning classroom became operational. The affordances of this space are intended to enable active teaching and learning in large classes. However, it is difficult to assess how successful this space is for active learning. A multi-year study has been undertaken to observe teaching practice in situ, with the goalof developing design principles for instructional space and technology that support the development, design, and implementation of teaching activities. Existing teaching observation protocols do not fully capture the interaction between the instructor and the space because such protocols were generally intended for other purposes. The goal is to develop a protocol that captures activities that are both intrinsic and extrinsic to teaching. This paper describes the development and use of an observation protocol. The core of the protocol is the wellknownTeaching Dimensions Observation Tool (TDOP). The scope of the TDOP is extended to active learning activities drawing from the Active Learning Classroom Observation Tool (ALCOT). The resulting extended protocol, TDOP+, was used for coding both live and recorded classroomobservations in the Winter 2020 term. This extended protocol allows the researchers to capture information that characterizes the intersection of pedagogy, space, and technology through Activity Theory. In future work, the data gathered through observations will be analyzed using theDifferentiated Overt Learning Activities (DOLA) framework, to provide insight into what types of teaching activity happens in a large-scale active learning classroom across STEM education and how active learning in large classrooms compares to pedagogy in other spaces.

https://ojs.library.queensu.ca/index.php/PCEEA/article/download/14927/9765

Susan McCahan

Papers

Identifying discipline-specific vocabulary on engineering exams

Engineering Vocabulary Development Using an Automated Software Tool

Usability Analysis of Freeform Marking on Engineering Problem Solving

Adaptation of a classroom observation protocol for active learning