A data science pipeline for educational data : a case study using learning catalytics in the active learning classroom.

This thesis presents an applied data science methodology on a set of University of Louisville, Speed School of Engineering student data. We used data mining and classic statistical techniques to help educational researchers quickly see the data trends and peculiarities. Our data includes scores and information about two Engineering Fundamental Class. The format of these classes is called an inverted classroom model or flipped class. The purpose of this study is to analyze the data in order to uncover potentially hidden information, tell interesting stories about the data, examine student learning behavior and learning performance in an active learning environment, including collaborative learning in a flipped classroom model

Scholarly insight Autumn 2017:a Data wrangler perspective

As the OU is going through several fundamental changes, it is important that strategic decisions made by Faculties and senior management are informed by evidence-based research and insights. One way how Data Wranglers provide insights of longitudinal development and performance of OU modules is the Key Metric Report 2017. A particular new element is that data can now also be unpacked and visualised on a Nation-level. As evidenced by the Nation-level reporting, there are substantial variations of success across the four Nations, and we hope that our interactive dashboards allow OU staff to unpack the underlying data. The second way Data Wranglers provide insight to Faculties and Units is through the Scholarly insight report series. Building on the previous two reports whereby we reported on substantial variation and inconsistencies in learning designs and assessment practices within qualifications across the OU, in this Scholarly insight Autumn 2017 report we address four big pedagogical questions that were framed and co-constructed together with the Faculties and LTI units. Many Faculties and colleagues have reacted positively on our Scholarly insight Spring 2017 report, whereby for the first time we were able to show empirically that students experienced substantial variations in success within 12 large OU qualifications. As evidenced in our previous report, 55% of variation in students’ success over time was explained by OU institutional factors (i.e., how students were assessed within their respective module; how students were able to effectively transition from one learning design of one module to the next one), rather than students’ characteristics, engagement and behaviour. We have received several queries and questions from Faculties and Units about how to better understand these students’ journeys, and how qualifications and module designs could be better aligned within their respective qualification(s). As these are complex conceptual and Big Pedagogy questions, in Chapter 1 we continued these complex analyses by looking at the transitional processes of the first two modules that OU students take, and how well aligned these modules and qualification paths are. In Chapter 2, we explored the more fine-grained, qualitative, and lived experiences of 19 students across a range of qualifications to understand how OU grading practices and (in)consistencies of assessment and feedback influenced their affect, behaviour, and cognition. In addition to building on previous topics, we introduced two new Scholarly insights in Chapter 3 and Chapter 4. As the OU is increasingly using learning analytics to support our staff and students, in Chapter 3 we analysed the impact of giving Predictive Learning Analytics to over 500 Associate Lecturers across 31 modules on student retention. Finally, in Chapter 4 we explored the impact of first presentations of new modules on pass rates and satisfaction, whereby we were able to bust another myth that may have profound implications for Student First Transformation. Working organically in various Faculty sub-group meetings and LTI Units and in a google doc with various key stakeholders in the Faculties , we hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our module designs and qualification pathways. Of course we are keen to hear what other topics require Scholarly insight

The CS Teacher Evaluation Model: Exploring Teacher and School Leader Perceptions and a Model for Improving Teacher and Student Growth

This study investigated the teacher evaluation model used by Concept-managed charter schools. The STEM-focused, college preparatory Concept Schools is a management company that manages 31 charter schools in seven different states in the U.S. The Concept Schools (CS) teacher evaluation model was based on the Danielson Framework and has been used in all CS-managed schools since 2011. This dissertation used data from three years of consecutive teacher evaluations scores to create a predicting model for K-12 school districts. The model analyzed an individual teacher’s performance growth and predicts future performance. The study used both qualitative and quantitative methods. The researcher interviewed seven teachers and six principals. For analysis of interview data, the thematic analysis method was used. The quantitative part of research was used to collect teachers’ evaluation scores and students’ standardized math and reading scores. The Structural Equation Modeling (SEM) and Analysis of Moment Structures (AMOS) used for statistical calculation included estimation of fit indices, errors, and model parameters. The study findings indicated that there was no overall significant relation between teacher evaluation scores and student standardized test scores. However, between year-two and year-three teachers there was a relation between evaluation scores and student standardized test scores. This study also highlights the importance of open dialogue between teacher and principal, meaningful feedback, non-core subjects training, and clears rubrics to support trust building between teachers and principals. Finally, findings indicated targeted or need-based professional development plans for teachers that could have more potential in an area of improvement

Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

2013 Walden University Research Symposium

Welcome to the 2013 Walden University Research Symposium. Again this year we are excited to be able to showcase research projects from our academic community, including work by our recent graduates, in two different formats. Poster presentations provide an opportunity for researchers to engage with all individuals attending the symposium and potentially to network with other interested researchers. Within the roundtable presentation format, a select group of researchers will be available for interactive discussions of their work, with handouts and visual materials available to support the discussion. The topics and approaches are diverse as usual, but this year we see more research being conducted outside of the United States. The roundtables sessions at this symposium are of particular note, however. This summer we are continuing with the theme from our Research Colloquium in January. Each roundtable highlights research that examines and supports translations and implementations of educational practices from the face-to-face to the online environment. We feel all attendees will find something of relevance to their own work. Also, be sure to note that editors from each of the four research journals that are supported by Walden University will be in attendance. If you are interested in these publication venues, you can get any questions answered. Finally, for the first time, the Research Symposium is now included on the slate of events for graduates who are in town for the ceremony. The magic of a research symposium can be found in the interactions between presenters and audience. So, please don’t be shy—step up, ask questions, make comments, and enjoy the experience. We also encourage you to use the Contacts page at the end of this program to record key connections that you will make at this event.https://scholarworks.waldenu.edu/archivedprograms/1006/thumbnail.jp

Exploring Potentials in Mobile Phone GPS Data Collection and Analysis

In order to support efficient transportation planning decisions, household travel survey data with high levels of accuracy are essential. Due to a number of issues associated with conventional household travel surveys, including high cost, low response rate, trip misreporting, and respondents’ self-reporting bias, government and private agencies are desperately searching for alternative data collection methods. Recent advancements in smart phones and Global Positioning System (GPS) technologies present new opportunities to track travelers’ trips. Considering the high penetration rate of smartphones, it seems reasonable to use smartphone data as a reliable source of individual travel diary. Many studies have applied GPS-Based data in planning and demand analysis but mobile phone GPS data has not received much attention. The Google Location History (GLH) data provide an opportunity to explore the potential of these data. This research presents a study using GLH data, including the data processing algorithm in deriving travel information and the potential applications in understanding travel patterns. The main goal of this study is to explore the potential of using cell phone GPS data to advance the understanding in mobility and travel behavior. The objectives of the study include: a) assessing the technical feasibility of using smartphones in transportation planning as a substitute of traditional household survey b) develop algorithms and procedures to derive travel information from smartphones; and c) identify applications in mobility and travel behavior studies that could take advantage of these smartphones GPS data, which would not have been possible with conventional data collection methods. This research aims to demonstrate how accurate travel information can be collected and analyzed with lower cost using smartphone GPS data and what analysis applications can be made possible with this new data source. Moreover, the framework developed in this study can provide valuable insights for others who are interested in using cell phone data. GLH data are obtained from 45 participants in a two-month period for the study. The results show great promise of using GLH data as a supplement or complement to conventional travel diary data. It shows that GLH provides sufficient high resolution data that can be used to study people’s movement without respondent burden, and potentially it can be applied to a large scale study easily. The developed algorithms in this study work well with the data. This study supports that transportation data can be collected with smartphones less expensively and more accurately than by traditional household travel survey. These data provide the opportunity to facilitate the investigation of various issues, such as less frequent long-distance travel, hourly variations in travel behavior, and daily variations in travel behavior

Customizing the promotion strategies of integrated air-bus service based on passenger satisfaction

The integrated air-bus service expands the catchment area and alleviates congestion of regional airports. To gain further insights into the unexplored potential attributes of the integrated service that generate passenger satisfaction, this paper utilizes a two-stage analysis approach to identify the key promotion factors for passengers from different constituents. Based on the survey data collected in Nanjing Lukou International Airport, this paper 1) uses k-means clustering to categorize respondents into four groups. 2) Combines the gradient boosting decision tree and impact asymmetry analysis to identify the attributes that have nonlinear influences on the overall service satisfaction for each group respectively. Results suggest that the timetable of the airport bus is critical for all passenger groups. Interestingly, there are noticeable differences in passenger satisfaction with the accessibility, cost affordability, comfort, reliability, and integration of the integrated service, providing the basis for customizing service promotion strategies among different passenger groups and airports.</p