A standards-based grading model to predict students\u27 success in a first-year engineering course

Using predictive modeling methods, it is possible to identify at-risk students early in the semester and inform both the instructors and the students. While some universities have started to use standards-based grading, which has educational advantages over common score-based grading, at–risk prediction models have not been adapted to reap the benefits of standards-based grading. In this study, seven prediction models were compared to identify at-risk students in a course that used standards-based grading. When identifying at-risk students, it is important to minimize false negative (i.e., type II) errors while not increasing false positive (i.e., type I) errors significantly. To increase the generalizability of the models and accuracy of the predictions, feature selection methods were used to reduce the number of variables used in each model. The Naive Bayes Classifier and an Ensemble model using a combination of models (i.e., Support Vector Machine, K-Nearest Neighbors, and Naive Bayes Classifier) had the best results among the seven tested models. This study identified possible threshold concepts and learning objectives that are important to students’ success in the course, and learning objectives that are not correlated with student success in the course

Comparing students\u27 solutions to an open-ended problem in an introductory programming course with and without explicit modeling interventions

Engineers must understand how to build, apply, and adapt various types of models in order to be successful. Throughout undergraduate engineering education, modeling is fundamental for many core concepts, though it is rarely explicitly taught. There are many benefits to explicitly teaching modeling, particularly in the first years of an engineering program. The research questions that drove this study are: (1) How do students\u27 solutions to a complex, open-ended problem (both written and coded solutions) develop over the course of multiple submissions? and (2) How do these developments compare across groups of students that did and did not participate in a course centered around modeling?. Students\u27 solutions to an open-ended problem across multiple sections of an introductory programming course were explored. These sections were all divided across two groups: (1) experimental group - these sections discussed and utilized mathematical and computational models explicitly throughout the course, and (2) comparison group - these sections focused on developing algorithms and writing code with a more traditional approach. All sections required students to complete a common open-ended problem that consisted of two versions of the problem (the first version with smaller data set and the other a larger data set). Each version had two submissions - (1) a mathematical model or algorithm (i.e. students\u27 written solution potentially with tables and figures) and (2) a computational model or program (i.e. students\u27 MATLAB code). The students\u27 solutions were graded by student graders after completing two required training sessions that consisted of assessing multiple sample student solutions using the rubrics to ensure consistency across grading. The resulting assessments of students\u27 works based on the rubrics were analyzed to identify patterns students\u27 submissions and comparisons across sections. The results identified differences existing in the mathematical and computational model development between students from the experimental and comparison groups. The students in the experimental group were able to better address the complexity of the problem. Most groups demonstrated similar levels and types of change across the submissions for the other dimensions related to the purpose of model components, addressing the users\u27 anticipated needs, and communicating their solutions. These findings help inform other researchers and instructors how to help students develop mathematical and computational modeling skills, especially in a programming course. This work is part of a larger NSF study about the impact of varying levels of modeling interventions related to different types of models on students\u27 awareness of different types of models and their applications, as well as their ability to apply and develop different types of models

Academic and Demographic Cluster Analysis of Engineering Student Success

Contribution: This article uses student semester grade point average (GPA) as a measure of student success to take into account the temporal effects in student success. The findings highlight the student performance based on their demographic status and use of university resources such as financial aid. College campuses should not only increase current resources but also raise awareness of current resources and make them more accessible (e.g., easier to apply or automatic applications). This is especially important for some demographics such as Hispanic first-generation students. Background: Higher education institutions are facing retention and graduation problems. One way to improve this is by understanding why students are not academically successful. Research Questions: In this study, demographic information and past academic records were analyzed to understand patterns of student success. Methodology: A cluster analysis was conducted to understand groups of students based on academic performance and demographic information. Examples of these factors are enrollment status, financial status, first-generation status, housing status, and transfer status. For the purpose of getting more accurate results, the students were separated into two different groups according to their admission status: 1) freshman and 2) transfer. Findings: The results indicate Hispanic, first-generation, low-income students are not likely to apply for financial aid although they are eligible. They have lower GPA and take fewer units per semester than other students. This can cause delayed graduation and accumulating more debt

Change in student understanding of modeling during first year engineering courses

All engineers must be able to apply and create models to be effective problem solvers, critical thinkers, and innovative designers. To be more successful in their studies and careers, students need a foundational knowledge about models. An adaptable approach can help students develop their modeling skills across a variety of modeling types, including physical models, mathematical models, logical models, and computational models. Physical models (e.g., prototypes) are the most common type of models that engineering students identify and discuss during the design process. There is a need to explicitly focus on varying types of models, model application, and model development in the engineering curriculum, especially on mathematical and computational models. This NSF project proposes two approaches to creating a holistic modeling environment for learning at two universities. These universities require different levels of revision to the existing first-year engineering courses or programs. The proposed approaches change to a unified language and discussion around modeling with the intent of contextualizing modeling as a fundamental tool within engineering. To evaluate student learning on modeling in engineering, we conducted pre and post surveys across three different first-year engineering courses at these two universities with different student demographics. The comparison between the pre and post surveys highlighted student learning on engineering modeling based on different teaching and curriculum change approaches

Student Awareness of Models in First-Year Engineering Courses

Contribution: This study assesses more than 800 students\u27 awareness of engineering model types before and after taking two first-year engineering courses across two semesters and evaluates the effect of each course. Background: All engineers must be able to apply and create models to be effective problem solvers, critical thinkers, and innovative designers. To help them develop these skills, as a first step, it is essential to assess how to increase students\u27 awareness of engineering models. According to Bloom\u27s taxonomy, the lower remember and understand levels, which encompass awareness, are necessary for achieving the higher levels, such as apply, analyze, evaluate, and create. Research Questions: To what extent did student awareness of model types change after taking introductory engineering courses? To what extent did student awareness of model types differ by course or semester? Methodology: In this study, a survey was designed and administered at the beginning and end of the semester in two first-year engineering courses during two semesters in a mid-sized private school. The survey asked students questions about their definition of engineering modeling and different types of models. Findings: Overall, student awareness of model types increased from the beginning of the semester toward the end of the semester, across both semesters and courses. There were some differences between course sections, however, the students\u27 awareness of the models at the end of the academic year was similar for both groups

Undergraduate and Graduate Teaching Assistants\u27 Perceptions of Their Responsibilities - Factors that Help or Hinder

Effective teaching assistants (TAs) are crucial for effective student learning. This is especially true in science, technology, engineering, and mathematics (STEM) programs, where TAs are enabling large programs to transition to more student-centered learning environments. To ensure that TAs are able to support these types of learning environments, their perspectives of training, their abilities, and other work related aspects must be understood. In this paper a survey that was created based on interviews conducted with eight TAs is discussed. The survey has four primary categories of content that are critical for understanding TAs\u27 perspectives: (1) background, (2) motivation, (3) training, and (4) grading and feedback. This research team is first utilizing this survey at Purdue University to test for validity and reliability of the instrument, as well as identifying ways to improve the experiences and effectiveness of the First-Year Engineering Program\u27s TAs\u27 support system, training, hiring process, and any other relevant components of the infrastructure. The more generalizable goal of this research is to further develop this survey to be used by any STEM program as a diagnostic tool for identifying opportunities to enhance the TA support systems and therefore improve student learning

Types of Models Identified by First-Year Engineering Students

This is a Complete Research paper. Understanding models is important for engineering students, but not often taught explicitly in first-year courses. Although there are many types of models in engineering, studies have shown that engineering students most commonly identify prototyping or physical models when asked about modeling. In order to evaluate students\u27 understanding of different types of models used in engineering and the effectiveness of interventions designed to teach modeling, a survey was developed. This paper describes development of a framework to categorize the types of engineering models that first-year engineering students discuss based on both previous literature and students\u27 responses to survey questions about models. In Fall 2019, the survey was administered to first-year engineering students to investigate their awareness of types of models and understanding of how to apply different types of models in solving engineering problems. Students\u27 responses to three questions from the survey were analyzed in this study: 1. What is a model in science, technology, engineering, and mathematics (STEM) fields?, 2. List different types of models that you can think of., and 3. Describe each different type of model you listed. Responses were categorized by model type and the framework was updated through an iterative coding process. After four rounds of analysis of 30 different students\u27 responses, an acceptable percentage agreement was reached between independent researchers coding the data. Resulting frequencies of the various model types identified by students are presented along with representative student responses to provide insight into students\u27 understanding of models in STEM. This study is part of a larger project to understand the impact of modeling interventions on students\u27 awareness of models and their ability to build and apply models

Design, implementation and testing of a visual discussion forum to address new post bias

One challenge of using asynchronous online discussions in educational settings is that students have a tendency to read only new posts and reply to just the most recent ones. This has a variety of negative consequences for learning through discussions. In this thesis, I used information visualization techniques to design a visual discussion forum interface and studied students’ behaviours using this visual forum as compared to a traditional text-based linear forum. A hyperbolic tree, which presents the higher-level posts with bigger nodes, was used to present the structure of the discussion. In the visual forum, students (re)read higher-level posts before their new replies. Additionally, students more actively selected which threads to read as compared to the text-based forum. Students’ pointed out the visual design and layout as one of the most useful features of the interface. However, students’ feedback raised concerns about some interface features that should be investigated further

Applied Computing for Behavioral and Social Sciences (ACBSS) Minor

The growing digital economy creates unprecedented demand for technical workers, especially those with both domain knowledge and technical skills. To meet this need, an ACBSS (Applied Computing for Behavioral and Social Sciences) minor degree has been developed by an interdisciplinary team of faculty at San José State University (SJSU). The minor degree comprises four courses: Python programming, algorithms and data structures, R programming, and culminating projects. The first ACBSS cohort started in Fall 2016 with 32 students, and the second cohort in Fall 2017 reached its capacity of 40 students, 62% of whom are female and 35% are underrepresented minority students. Considering ACBSS students’ interest in human behavior and society, pedagogical approaches using relevant examples and projects have been developed and integrated throughout the program. Preliminary assessments show that students appreciated learning programming skills with which to expand their career opportunities while gaining confidence in studying technical subjects. These results show that ACBSS, an interdisciplinary computing education program, offers a promising model in providing computing education to more diverse students for the 21st-century digital workplace