eLearning Lab

Permanent link for this collectionhttps://hdl.handle.net/2022/25167

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Now showing 1 - 20 of 21
  • Item
    Data Extract: Jacobson 23
    (2023-04) Jacobson, Erik; Quick, Joshua; Motz, Benjamin
    This entry describes queries to obtain a large number of discussion posts and corresponding metadata from Canvas discussions associated with assignments from 2018-2022. Additionally, the entry describes de-identification procedures to anonymize posters for research and privacy compliance.
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    DATA EXTRACT: Wong Goldstone23
    (2023-02) Wong, Vincent; Goldstone, Robert L.; Motz, Benjamin A.; Quick, Joshua D.
    This data request provided data for a study into the relationship between individual performance on group performance and group performance on individual performance. In the context of a single course offered between Fall 2017 and Fall 2019, student enrollment, submissions scores, and group membership are provided in a de-identified data set.
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    Data Extract: Strackeljahn Candy23
    (2023-02) Strackeljahn, Andi M.; Candy, T. Rowan; Quick, Joshua D.
    This entry contains scripts for extracting Canvas events, Kaltura summary data, and Quickcheck data for an onboarding course in Optometery.
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    Data Extract: Motz23
    (2023-02-23) Motz, Benjamin; Quick, Joshua
    This data extraction contains SQL script for the development of datasets on how the Boost nudging system influenced student interactions with course assessments and their performance on these assessments. Data includes assignment views, times between submission and assignment due date, assignment score, and overall course score.
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    DATA EXTRACT: Xiao22
    (2022-06-17) Quick, Joshua; Xiao, Ruli; Kaganovich, Michael; Motz, Ben
    This item is an extension of the Xiao21 data extract: https://hdl.handle.net/2022/26984. This item describes queries to develop tables on weekly activity within Canvas and provides joinable tables with obfuscated enrollment and student retention tables.
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    DATA EXTRACT: Husmann22;1
    (2022-06-15) Quick, Joshua; Husmann, Polly; Motz, Ben
    This item contains data extraction processes for a preliminary study on the influence of Kaltura video engagement on students' performance in an introductory human anatomy course. Additionally, processes for combining Top Hat and Canvas data are included. The aim of this study was to identify students' self-reported engagement with Kaltura and analytics and the relations between these measures and outcomes.
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    DATA EXTRACT: SACAI-HOSSAIN22
    (2022) Quick, Joshua; Hossain, Md Nour; Motz, Ben
    This item describes the specified queries to fulfill this request and the transformation of these outputs through R.
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    DATA EXTACT: JOHNSON 21/22
    (2022-01-07) Quick, Joshua; Johnson, Sarah; Motz, Benjamin
    This item contains queries to generate data for students' interactions within Canvas course sites and Canvas modules to identify differences between students and conditions in which course content was differentially made available.
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    DATA Extract: Xiao21
    (2021-12-08) Quick, Joshua; Motz, Ben; Xiao, Ruli; Kaganovich, Michael
    The unique circumstances of spring semester of 2020 and the disruptions they caused to teaching and learning, have led to a unique grading option for students in classes taken that semester. Specifically, upon learning their standard letter grade for a course prior to its final submission to the Registrar, students were allowed to have it replaced with “S”, or “Satisfactory” grade. This situation creates a unique opportunity to (1) gain insights in student choices of preferred assessment of their performance and (2) to analyze the impact of the grading experiment and the underlying disruption to learning on subsequent academic performance. The questions we will investigate, in the context of Business and Economics in comparison to other disciplines, will include the following. First, how does a student’s ultimate choice of “S” grade in the spring of 2020 relate to this student’s academic and other characteristics and a regular letter grade the student would otherwise receive or was expected to receive? The roles that student gender and other socioeconomic characteristics play in grade option choices are of special interest in this regard. Second, what are the consequences of the “S” grade option in introductory level classes on student retention in the major in the short-run, and subsequent outcomes in higher level classes in the longer-term? The broader insights we wish to gain is to draw a distinction between the effect of disruption to student learning in spring 2020 intro classes vs. the consequences of effectively lowered grade bar to make students eligible for higher level classes in their discipline.
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    DATA_EXTRACT: SACKS_21
    (2021-12-08) Quick, Joshua; Motz, Ben; Sacks, Dan
    This item contains the SQL queries used to create views for a research data request on student enrollments from Fall 2014-Fall 2021. The intention of this data request is to understand the extent to which students retain within their academic program based off of outcomes for STEM & Business/Behavioral Economic courses and the relations between student demographic information (e.g., Gender, Ethnicity, etc.). Other introductory courses are examined to monitor the influence of STEM/BE courses.
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    Canvas Assignment Factors 2019
    (2021-09) Quick, Joshua; Motz, Ben
    This entry describes the queries used to construct an assignment factor breakdown within Canvas for Fall 2019 courses for purposes of institutional research and instruction.
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    Data Extract: Hickey21_Expansive_Framing
    (2021) Quick, Joshua; Hickey, Daniel; Motz, Ben
    The purpose of this study is to understand and evaluate the extent to which situative learning design features implemented in online course contexts enable learners to expansively frame and productively engage in disciplinary practices and the extent to which these practices enable learner transfer of disciplinary knowledge. Specifically, this study seeks to understand which features tend to facilitate these engagements and the extent to which these engagements are productive for learners in terms of their transfer and application of disciplinary knowledge. This study will examine the social interactions of students and instructors within an online context and the relation between these interactions and student performance on exams.
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    DATA_EXTRACT_SACAI_ELLIOT21
    (2021-08-20) Quick, Joshua; Elliot, Rob; Motz, Ben
    There appears to be a disconnect between the implementation of technology and digital resources by instructors and the technology that students use for their academic work (Gierdowski, 2019). The distribution of course materials via a learning management system (LMS) is done so in a top-down fashion where the instructor has control over the format and delivery method (Mpungose & Khoza, 2020; Schoonenboom, 2014). Supplemental documents, course videos, discussions, and a variety of digital learning tools (e.g., interactive exercises) are used in concert with readings to produce a holistic package of content that ushers the student toward successful completion of learning objectives (Henderson et al., 2017). This style of technology-enhanced learning is supposed to provide more control to the learners by affording them more flexibility in the time and space where they engage with the content (Laurillard, 2002; Taylor et al., 2006). But if the technology through which the material is delivered does not match the technology through which the students are able to or choose to engage with it, then the benefits of the technology-enhanced learning are diminished or lost (Taylor et al., 2006). This study investigates the use of mobile devices by students in higher education to support their formal learning processes
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    DATA EXTRACT: English Composition & Social Annotations: Hodgson21
    (2021) Quick, Joshua; Hodgson, Justin; Motz, Benjamin; Israel, Jamie
    The primary objective of this study is to investigate the relationship between student participation in Social Annotation (SA) and their subsequent writing practices. The secondary objective is to investigate how students participate in SA as a form of writing, social interaction, and meaning-making in multiple course contexts. The tertiary purpose of this study is to investigate how instructors plan, facilitate, and support student participation in both SA and other writing activities.This item describes the process and queries used to generate de-identified data sets of 65 Spring 2021 courses that used the Hypothes.is social annotations tool.
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    DATA EXTRACT: Incidence of Collaborative Tool Utilization in Canvas Course Sites for Different Disciplines
    (2021) Diekman, Amanda; Motz, Benjamin; Joshi, Mansi
    Past work has shown that when opportunities to work together are present within a role, students tend to report greater belonging and interest in their careers. Yet, when it comes to STEM careers vs non-STEM careers, people tend to believe that these communal opportunities are lacking. Here we are interested to understanding how subdisciplines in STEM provide these communal opportunities of working together and connecting with others. We are interested in the following question: Do STEM courses enable collaborative tools within their canvas sites? Does the use of collaborative tools differ by STEM discipline? These queries pull data from collaborative tools, and aggregates these data at the course-level, with metadata about the course level, format, and inventory.
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    DATA EXTRACT: Discussion Posts & Replies-Hickey
    (2020) Quick, Joshua; Motz, Ben; Hickey, Daniel
    The purpose of this study is to understand how course design principles impact inclusive, diverse, personal, and culturally and/or politically framed disciplinary engagement and learning in online courses and to refine these features and the designs principles underlying them. Specifically, this study intends to understand the ways in which these background and experiences are used to engage with and apply disciplinary content and practices in students’ personal & professional contexts. The study will examine the social interactions of the students and the instructors. These queries pull all data from discussion posts submitted within the specified courses as part of the assignment in addition to the corresponding discussion replies from both students and instructors.
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    DATA EXTRACT: Canvas Events & Interface-Elliott
    (2021) Quick, Joshua; Motz, Ben; Elliott, Rob
    This study seeks to examine the automated log files generated by user interactions with the Canvas Learning Management System (LMS) to investigate usage patterns specifically related to the device (computer or mobile) and interface (website or mobile app) used by the individual. This item describes the query used to generate a data set for the investigator to construct an analytic process for understanding how students are accessing Canvas content.
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    Going Remote: Actionable Insights from Indiana University’s Transition to Remote Instruction due to COVID-19
    (2020-07) Motz, Benjamin; Jankowski, Harmony; Snapp-Childs, Winona; Quick, Joshua
    On March 10, 2020, Indiana University (IU) announced the suspension of in-person instruction due to COVID-19. At that time, the eLearning Research and Practice Lab, a laboratory within the Indiana University Pervasive Technology Institute, began preparing to conduct a full-census survey of all undergraduates and instructors across all IU campuses. The study’s purpose was to examine student and instructor experiences of the transition to remote instruction, and to identify actionable insights that may improve instruction during future semesters. This report was prepared and distributed internally at IU, in order to provide rapid evidence-based recommendations for instructional practice.
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    DATA EXTRACT: Student eLearning Metrics Joined with COVID-19 Survey Responses
    (2020) Motz, Benjamin; Quick, Joshua
    The COVID-19 pandemic caused Indiana University, like many colleges and universities across the United States, to suspend in-person classes, transitioning fully and abruptly to online learning. This situation offered a unique opportunity to understand how the exclusive use of online education affected course instruction and student learning and behavior. To advance this understanding, the eLearning Research and Practice Lab carried out a full census survey of all Indiana University undergraduate students and instructors of undergraduate courses during Summer 2020. In the survey, some student respondents provided an affirmative Release of Academic Information information, permitting the eLearning Lab to extract information about their activity and grades within the Canvas learning management system (LMS). This item includes technical details of this data extract, and its append to the survey responses.
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    DATA EXTRACT: SP20-FA20 Targeted Advising with Canvas Data Study
    (2020) Quick, Joshua; Motz, Benjamin; Rust, Matthew; Gladden, James; Buyarski, Catherine
    Currently at IUPUI, advising staff make periodic contact with students who are at risk for dropping out of college. At present, identification of at-risk students is inferred from student demographics, past performance, high school performance, and from flags raised by their teachers in Indiana University’s “Student Engagement Roster.” However, these measures do not reveal more timely indicators of student difficulties in their course work. Thus, the current study aims to evaluate whether the targeting of at-risk students could be augmented by including near-real-time measures of students’ behaviors in their course sites administrated via Canvas. This item describes the queries and metadata for this research project