Bourdieu, networks, and movements: Using the concepts of habitus, field and capital to understand a network analysis of gender differences in undergraduate physics

Current trends suggest that significant gender disparities exist within Science, Technology, Engineering, and Mathematics (STEM) education at university, with female students being underrepresented in physics, but more equally represented in life sciences (e.g., biology, medicine). To understand these trends, it is important to consider the context in which students make decisions about which university courses to enrol in. The current study seeks to investigate gender differences in STEM through a unique approach that combines network analysis of student enrolment data with an interpretive lens based on the sociological theory of Pierre Bourdieu. We generate a network of courses taken by around 9000 undergraduate physics students (from 2009 to 2014) to quantify Bourdieu's concept of field. We explore the properties of this network to investigate gender differences in transverse movements (between different academic fields) and vertical movements (changes in students' achievement rankings within a field). Our findings indicate that female students are more likely to make transverse movements into life science fields. We also find that university physics does a poor job in attracting high achieving students, and especially high achieving female students. Of the students who do choose to study physics, low achieving female students are less likely to continue than their male counterparts. The results and implications are discussed in the context of Bourdieu's theory, and previous research. We argue that in order to remove constraints on female student's study choices, the field of physics needs to provide a culture in which all students feel like they belong.Comment: 23 pages, 6 figures, 1 tabl

Modelling math learning on an open access intelligent tutor

This paper presents a methodology to analyze large amount of students’ learning states on two math courses offered by Global Fresh- man Academy program at Arizona State University. These two courses utilised ALEKS (Assessment and Learning in Knowledge Spaces) Arti- ficial Intelligence technology to facilitate massive open online learning. We explore social network analysis and unsupervised learning approaches (such as probabilistic graphical models) on these type of Intelligent Tu- toring Systems to examine the potential of the embedding representa- tions on students learning

Economic Education’s Roller Coaster Ride In Hawaii, 1956-2006

During the early 1960s a few of Hawaii’s public high schools began to offer economics courses, and they gradually became popular social studies electives. By 1999, over 46% of public high school seniors completed a one-semester course in economics. From this peak, enrollment rates would plummet to just 11% in 2003, before rebounding to 27% in 2005 and 2007. Our analysis searches for an explanation by identifying large changes in key variables and public policies that determine demand for and supply of economic education in Hawaii’s schools. We conclude that changes in the incentives facing large Hawaii businesses, University of Hawaii faculty and administrators, and bureaucrats in the State of Hawaii Department of Education have reduced the supply of qualified teachers and student enrollment rates.economic education, Hawaii, enrollment, network externality

Analisis dan Pemetaan Mata Kuliah Bersyarat Program Studi Fisika Menggunakan BackPropagation Neural Network

This study aims to analyze and map the conditional courses at the Tadris Physics Study Program, Faculty of Tarbiyah and Teacher Training, Sulthan Thaha Saifuddin State Islamic University Jambi. This research is an applied science research, data analysis using quantitative descriptive technique. The data is in the form of documenting the value of the 2019/2020 Tadris Physics Study Program students. The research sample consisted of 11 sample subjects from 19 population subjects. The data is processed using Backpropagation Neural Network with Python programming language. Validation and accuracy of prediction results using Mean Absolute Percentage Error and determinant coefficient R Square. The prediction results of conditional courses obtained are accurate and valid with MAPE values <10% (very good) and R Square values close to 1. This study shows that the mapping of prerequisite courses set by the study program is appropriate, except for Basic Physics Courses. 2 (R 0.216) and Mathematics Physics Course I (R 0.50) require additional other prerequisite courses.Keywords: mapping; conditional courses, backpropagation neural networ

HarvardX and MITx: Two Years of Open Online Courses Fall 2012-Summer 2014

What happens when well-known universities offer online courses, assessments, and certificates of completion for free? Early descriptions of Massive Open Online Courses (MOOCs) have emphasized large enrollments, low certification rates, and highly educated registrants. We use data from two years and 68 open online courses offered by Harvard University (via HarvardX) and MIT (via MITx) to broaden the scope of answers to this question. We describe trends over this two-year span, depict participant intent using comprehensive survey instruments, and chart course participation pathways using network analysis. We find that overall participation in our MOOCs remains substantial and that the average growth has been steady. We explore how diverse audiences — including explorers, teachers-as-learners, and residential students — provide opportunities to advance the principles on which HarvardX and MITx were founded: access, research, and residential education

Mixing and Matching Learning Design and Learning Analytics

In the last five years, learning analytics has proved its potential in predicting academic performance based on trace data of learning activities. However, the role of pedagogical context in learning analytics has not been fully understood. To date, it has been difficult to quantify learning in a way that can be measured and compared. By coding the design of e-learning courses, this study demonstrates how learning design is being implemented on a large scale at the Open University UK, and how learning analytics could support as well as benefit from learning design. Building on our previous work, our analysis was conducted longitudinally on 23 undergraduate distance learning modules and their 40,083 students. The innovative aspect of this study is the availability of fine-grained learning design data at individual task level, which allows us to consider the connections between learning activities, and the media used to produce the activities. Using a combination of visualizations and social network analysis, our findings revealed a diversity in how learning activities were designed within and between disciplines as well as individual learning activities. By reflecting on the learning design in an explicit manner, educators are empowered to compare and contrast their design using their own institutional data

ANALYZING STUDENTS’ COURSE EVALUATIONS USING TEXT MINING: VISUALIZATION OF OPEN-ENDED RESPONSES IN A CO-OCCURRENCE NETWORK

Japan’s Standards for Establishment of Universities states, “A university shall conduct organized training and research to improve the content and methodology used in courses at said university.” Based on this, most of Japan’s universities have recently implemented course evaluations by students. Student course evaluations are intended to quantify and provide an understanding of students’ satisfaction with their courses, and all universities are implementing them as one way to objectively evaluate courses. These course evaluations often combine computer-graded multiple-choice items with open-ended items. Computer-graded multiple-choice items are easy to assess because the responses are quantifiable. However, open-ended items’ responses are text data, and objectively grasping the students’ general tendencies is challenging. Moreover, it is difficult to avoid risking arbitrary and subjective interpretations of the data by the analysts who summarize them. Therefore, to avoid these risks as much as possible, the so-called “text-mining” method or “quantitative content analysis” approach might be useful. This study shares our experiences using text mining to analyze students’ course evaluations through the visualization of their open-ended responses in a co-occurrence network, and we discuss the potential of this method.&nbsp

Social Network Analysis and Resilience in University Students: An Approach from Cohesiveness

13 p.The Social Network Analysis offers a view of social phenomena based on interactions. The aim of this study is to compare social reality through the cohesion variable and analyse its relationship with the resilience of university students. This information is useful to work with the students academically and to optimise the properties of the network that have an influence in academic performance. This is a descriptive transversal study with 90 students from the first and third year of the Nursing Degree. Cohesion variables from the support and friendship networks and the level of resilience were gathered. The UCINET programme was used for network analysis and the SPSS programme for statistical analysis. The students’ friendship and support networks show high intra-classroom cohesion although there are no differences between the support networks and friendship or minimal contact networks in both of the courses used for the study. The network cohesion indicators show less cohesion in the third year. No correlations were found between cohesion and resilience. Resilience does not appear to be an attribute related to cohesion or vice versa.S

Data Analytics of University Student Records

Understanding the proper navigation of a college curriculum is a daunting task for students, faculty, and staff. Collegiate courses offer enough intellectual challenge without the unnecessary confusion caused by course scheduling issues. Administrative faculty who execute curriculum changes need both quantitative data and empirical evidence to support their notions about which courses are cornerstone. Students require clear understanding of paths through their courses and majors that give them the optimal chance of success. In this work, we re-envision the analysis of student records from several decades by opening up these datasets to new ways of interactivity. We represent curricula through a graph of interconnected courses, studying correlations between student grades. This opens up possibilities for discovering intellectual prerequisites not shown in the course catalog. Extending this, we define a similarity metric for majors within the university, performing hierarchical clustering to reveal structure within this graph of majors not even present within the catalog. Lastly, we seek to show the temporal development of majors as the network grows through time. Through these approaches, our work provides improvements to current methods of viewing and interacting with student records

Correlations between student connectivity and academic performance: a pandemic follow-up

Social network analysis (SNA) has been gaining traction as a technique for quantitatively studying student collaboration. We analyze networks, constructed from student self-reports of collaboration on homework assignments, in two courses from the University of Colorado Boulder and one course from the Colorado School of Mines. All three courses occurred during the COVID-19 pandemic, which allows for a comparison between the course at the Colorado School of Mines (in a fully remote format) with results from a previous pre-pandemic study of student collaboration at the Colorado School of Mines (in a hybrid format). We compute nodal centrality measures and calculate the correlation between student centrality and performance. Results varied widely between each of the courses studied. The course at the Colorado School of Mines had strong correlations between many centrality measures and performance which matched the patterns seen in the pre-pandemic study. The courses at the University of Colorado Boulder showed weaker correlations, and one course showed nearly no correlations at all between students' connectivity to their classmates and their performance. Taken together, the results from the trio of courses indicate that the context and environment in which the course is situated play a more important role in fostering a correlation between student collaboration and course performance than the format (remote, hybrid, in-person) of the course. Additionally, we conducted a short study on the effect that missing nodes may have on the correlations calculated from the measured networks. This investigation showed that missing nodes tend to shift correlations towards zero, providing evidence that the statistically significant correlations measured in our networks are not spurious.Comment: Submitted to Phys. Rev. PE