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Engineering Education is a developing field, with new research and ideas constantly emerging and contributing to the ever-evolving nature of this discipline. Textual data (such as publications, open-ended questions on student assignments, and interview transcripts) form an important means of dialogue between the various stakeholders of the engineering community. Analysis of textual data demands consumption of a lot of time and resources. As a result, researchers end up spending a lot of time and effort in analyzing such text repositories. While there is a lot to be gained through in-depth research analysis of text data, some educators or administrators could benefit from an automated system which could reveal trends and present broader overviews for given datasets in more time and resource efficient ways. Analyzing datasets using Natural Language Processing is one solution to this problem. The purpose of my doctoral research was two-pronged: first, to describe the current state of use of Natural Language Processing as it applies to the broader field of Education, and second, to demonstrate the use of Natural Language Processing techniques for two Engineering Education specific contexts of instruction and research respectively. Specifically, my research includes three manuscripts: (1) systematic review of existing publications on the use of Natural Language Processing in education research, (2) automated classification system for open-ended student responses to gauge metacognition levels in engineering classrooms, and (3) using insights from Natural Language Processing techniques to facilitate exploratory analysis of a large interview dataset led by a novice researcher. A common theme across the three tasks was to explore the use of Natural Language Processing techniques to enable the computer to extract meaningful information from textual data for Engineering Education related contexts. Results from my first manuscript suggested that researchers in the broader fields of Education used Natural Language Processing for a wide range of tasks, primarily serving to automate instruction in terms of creating content for examinations, automated grading or intelligent tutoring purposes. In manuscripts two and three I implemented some of the Natural Language Processing techniques such as Part-of-Speech tagging and tf-idf (text frequency-inverse document frequency) that were found (through my systematic review) to be used by researchers, to (a) develop an automated classification system for student responses to gauge their metacognitive levels and (b) conduct an exploratory novice led analysis of excerpts from interviews of students on career preparedness, respectively. Overall results of my research studies indicate that although the use of Natural Language Processing techniques in Engineering Education is not widespread, although such research endeavors could facilitate research and practice in our field. Particularly, this type of approach to textual data could be of use to practitioners in large engineering classrooms who are unable to devote large amounts of time to data analysis but would benefit from algorithmic systems that could quickly present a summary based on information processed from available text data. / Ph. D. / Textual data (such as publications, open-ended questions on student assignments, and interview transcripts) form an important means of dialogue between the various stakeholders of the engineering community. However, analyzing these datasets can be time consuming as well as resource-intensive. Natural Language Processing techniques exploit the machine’s ability to process and handle data in time-efficient ways. In my doctoral research I demonstrate how Natural Language Processing techniques can be used in the classrooms and in education research. Specifically, I began my research by systematically reviewing current studies describing the use of Natural Language Processing for education related contexts. I then used this understanding to inform use of Natural Language Processing techniques to two Engineering Education specific contexts: one in the classroom to automatically classify students’ responses to open-ended questions to understand the metacognitive levels, and the second context of informing analysis of a large dataset comprising excerpts from interview transcripts of engineering students describing career preparedness.

published_or_final_version / Education / Master / Master of Education

M.Ed. / This research study focuses on researching the success of drama as intervention — and educational medium in the alteration of perceptions held regarding male homosexuality. The study is motivated by the fact that discrimination against males with this sexual orientation has evolved into a part of South African's everyday lives. The study is completed within the framework of the Existential paradigm. In the achievement of the above-mentioned study goal, the researcher applied the techniques of Qualitative research, and specifically used phenomenological studies according to this paradigm. The data was gathered through a variety of techniques (questionnaires, sketches and in-depth personal interviews) which enhanced the viability and reliability of the study through Triangulation. The drama, named "Die Voelverslcrikker" (with its own educational implications), was written according to the style of Absurd theatre, which projected the Existentialist philosophy in art. The drama was shown to the six people who participated in the study. Analyzing these responses gathered before the drama was seen, led to the identification of phenomenons, which were categorized. This proved to the researcher that the participants showed little insight into male homosexuality. It also enhanced the fact that negative attitudes toward male homosexuals were held. These findings justified the purpose of the study. These results were compared with the participants' responses after the play was seen, which led to conclusions regarding growth and change in insight regarding male homosexuality, bat a stagnancy in attitude towards males with this sexual orientation. These results were explained according to the Existential paradigm. In this regard the researcher mentioned fusion, the perceptual neuropsychological organization, introspection and Pragmatism. The researcher also proposes that this study presents a lot of other research opportunities.

The Employment Training Program has been mandated by the Federal Government to train and find unsubsidized employment for economically disadvantaged individuals and others facing serious barriers to employment. Keyboarding skills are of paramount importance in the clerical program. This study was undertaken to determine the effect of educational kinesiology on typewriting speed and accuracy for students with and without functional visual efficiency. It involved 75 subjects from the Employment Training Center (JTPA Program) in Arlington, Virginia. Typewriting pretests were administered to determine subjects speed and error averages. Eye screening was administered utilizing the Titmus Vision Tester and the King-Devick Saccade Test. Although the battery of tests for the Titmus were administered, only the lateral and vertical phoria near test that measures muscle balance, the relationship of the image of each eye to that of its fellow, provided meaningful data for this study. The K-D Test measures tracking ability of the eyes as they perceive copy for typewriting. Subjects were considered as functionally visually efficient by passing both vision tests. The concepts of educational kinesiology (education through movement) were explained to the subjects as a way to increase typing speed, reduce errors, and control stress. Volunteers for the training numbered 31; 44 subjects remained in the control group. The same straight-copy timed writing was used for the pretest and posttest. Analyses of variance were used to examine differences in pretest and posttest scores for both speed and accuracy. Some of the major findings of the study were as follows: 1. Training in educational kinesiology had no effect on typewriting speed and accuracy. 2. Functional visual efficiency had no effect on typewriting speed and accuracy. 3. No interaction exists between the typewriting pretest-posttest speed and accuracy scores for students who did and did not receive educational kinesiology training and for students with and without functional visual efficiency. / Ed. D.

In longitudinal settings, modeling the covariance structure of repeated measure data is essential for proper analysis. The first paper in this three-paper dissertation presents a survey of four journals in the fields of Education and Psychology to identify the most commonly used methods for analyzing longitudinal data. It provides literature reviews and statistical details for each identified method. This paper also offers a summary table giving the benefits and drawbacks of all the surveyed methods in order to help researchers choose the optimal model according to the structure of their data. Finally, this paper highlights that even when scholars do use more advanced methods for analyzing repeated measure data, they very rarely report (or explore in their discussions) the covariance structure implemented in their choice of modeling. This suggests that, at least in some cases, researchers may not be taking advantage of the optimal covariance patterns. This paper identifies a gap in the standard statistical practices of the fields of Education and Psychology, namely that researchers are not modeling the covariance structure as an extension of fixed/random effects modeling. The second paper introduces the General Serial Covariance (GSC) approach, an extension of the Linear Mixed Modeling (LMM) or Hierarchical Linear Model (HLM) techniques that models the covariance structure using spatial correlation functions such as Gaussian, Exponential, and other patterns. These spatial correlations model the covariance structure in a continuous manner and therefore can deal with missingness and imbalanced data in a straightforward way. A simulation study in the second paper reveals that when data are consistent with the GSC model, using basic HLMs is not optimal for the estimation and testing of the fixed effects. The third paper is a tutorial that uses a real-world data set from a drug abuse prevention intervention to demonstrate the use of the GSC and basic HLM models in R programming language. This paper utilizes variograms (a visualization tool borrowed from geostatistics) among other exploratory tools to determine the covariance structure of the repeated measure data. This paper aims to introduce the GSC model and variogram plots to Education and Psychology, where, according to the survey in the first paper, they are not in use. This paper can also help scholars seeking guidance for interpreting the fixed effect-parameters.

In both engineering and physics education, a common objective is that students should learn to use theories and models in order to understand the relation between theories and models, and objects and events, and to develop holistic, conceptual knowledge. During lab-work, students are expected to use, or learn to use, symbolic and physical tools (such as concepts, theories, models, representations, inscriptions, mathematics, instruments and devices) in order both to understand the phenomena being studied, and to develop the skills and abilities to use the tools themselves. We have earlier argued that this learning should be seen as the learning of a complex concept, i.e. a “concept” that makes up a holistic system of “single” interrelated “concepts” (i.e. a whole made up of interrelated parts). On the contrary, however, in education research it is common to investigate “misconceptions” of “single concepts”. In this paper we will show the power of analysing engineering students’ learning as the learning of a complex concept. In this model “single concepts” are illustrated as nodes or “islands” that may be connected by links, while the links that students actually make are represented by arrows. The nodes in our model are found by looking for “gaps” in the actions and conversations of students. A gap corre­sponds to a non-established link, and when a gap is filled and the students establish a relation between two nodes, this is represented by a link. The more links that are made, the more complete the knowledge. In this study we report an analysis of a sequence of labs about AC-electricity in an electric circuit theory course. In for example electric circuit theory the “concepts” of current, voltage and impedance are interdependent. Rather, the central physical phenomenon is “electricity” represented by Ohms law as a generalization of the current/voltage/impedance/frequency-relationship of a circuit or circuit element. The results show the learning of “electricity” as a complex concept with students’ knowledge becoming more complete. Furthermore, according to our analysis “entities” that in later labs were fused into one were separate in the earlier labs. For example in a later lab we could note that “the physical circuit” and “the circuit drawing” had fused into a single “real circuit”. Our results suggest that the learning of a complex concept first start with establishing more and more links. As links become well established, “entities” that have been separate fuse into a whole. Our model suggests a method for finding “learning difficulties” since these corresponds to “gaps” and non-established links. As teachers and experts in a field we can miss to uncover these since for us the ‘complex concept’ has become a conceptual whole and we may no longer be able to distinguish the parts in the complex. In line with the thesis of M. Holmberg we also argue that learning problems in electric circuit theory may be due to the common failure to appreciate that concepts are relations.

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