Customized course advising

Nam, Sungjin; Lonn, Steven; Brown, Tony N.; Davis, Cinda-Sue; Koch, Darryl

doi:10.1145/2567574.2567589

Cited by 20 publications

(11 citation statements)

References 9 publications

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“…Limited research does demonstrate that when students are taking a STEM course perceived to be difficult, enrollment in a second difficult course predicts a performance decrease in the first course, 64 and that engineering students' performance in precalculus may be weakened when concurrently taking a chemistry course versus taking another engineering course. 65 ■ RESEARCH QUESTIONS This study combined four major predictive factors that have been extensively studied independently within the literature for their roles in predicting undergraduate general chemistry success. These included prior course grades, mathematicsrelated factors, collaborative learning, and student demographics.…”

Section: ■ Review Of the Literaturementioning

confidence: 99%

What Makes a Grade? Harnessing Demographic and Supportive Factors to Predict General Chemistry I Completion

McCarren,

Adams,

McNeilly

2024

J. Chem. Educ.

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General Chemistry I is a gateway course for first-year undergraduate STEM students. However, many students are underprepared for this course and ultimately do not succeed, despite remediation efforts. Transcript data from an R1 institution show that students who take a fall preparatory course before entering spring General Chemistry I perform better than students who do not take the remedial course. However, of all 4,500 students completing the remedial course between 2017 and 2021, only 64% subsequently completed spring General Chemistry I. Binary logistic regression was used to identify predictors in four areas (remedial course performance, mathematics-related factors, collaborative learning program participation, and demographics) relating to which remedial course students ultimately completed General Chemistry I. Results show that the strongest predictor of successful completion was remedial course grade. Race, first generation status, and concurrent mathematics enrollment were not predictors of completion for students earning similar remedial course grades, although calculus preparation and collaborative learning program participation increased completion probability. Additionally, multiple linear regression was performed to predict remedial course GPA. Race, first generation status, mathematics preparation, concurrent mathematics enrollment, and collaborative learning program participation were significant predictors of remedial course GPA. Results support the continued use of collaborative learning programs to close demographic gaps in course performance and show that students who were unprepared for calculus performed significantly worse in chemistry if concurrently taking remedial mathematics versus no math course. Outcomes suggest that time and content demands of remedial mathematics courses without deliberate chemistry curricular connections may negatively impact student chemistry performance.

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Section: ■ Review Of the Literaturementioning

confidence: 99%

What Makes a Grade? Harnessing Demographic and Supportive Factors to Predict General Chemistry I Completion

McCarren,

Adams,

McNeilly

2024

J. Chem. Educ.

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“…As detailed by Lonn, McKay, & Teasley (2017), U-M had built a nascent learning analytics community with many research projects being launched to investigate a variety of questions related to student learning and academic success. For example, one project revealed that first-year engineering students who received a 4 on their Math AB Advanced Placement (AP) exam only received an A or B grade 50% of the time when enrolling in Calculus II their first term (Nam, Lonn, Brown, Davis, & Koch, 2014). A different project analyzed the impact of a general chemistry prerequisite on later student achievement and progression in subsequent chemistry courses (Shultz, Winschel, & Gottfried, 2015).…”

Section: Institutional Context and Rationalementioning

confidence: 99%

Rearchitecting Data for Researchers: A Collaborative Model for Enabling Institutional Learning Analytics in Higher Education

Lonn

Koester

2019

Learning Analytics

Self Cite

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This article presents the case of the Learning Analytics Architecture (LARC) dataset, a collaborative effort at the University of Michigan to develop a common and extensible tool using administrative data and designed primarily for learning analytics researchers to investigate enrolled students' academic careers, demographics, and related teaching and learning outcomes. The institutional context prior to the creation of the dataset and the rationale, design, development, and maintenance involved in creating LARC are all detailed. Also discussed are the procedures for access, documentation, and ensuring the continued usability and relevance of the dataset for a growing learning analytics and data science research community. The authors conclude the case description with recommendations for institutions seeking to replicate this effort. Notes for Practice• Administrative data in higher education is not typically ready "out of the box" for learning analytics researchers. How can institutions leverage their own resources and knowledge of their data to reduce time-intensive and repetitive data-cleaning efforts to make the most of their own data?• This article presents the case study of the LARC dataset, as well as the decision points and process that the committee members undertook to build and maintain the data. This kind of effort remains a major hurdle for many higher education institutions.• A framework is provided for other institutions interested in building a similar dataset for learning analytics research. Six areas of consideration are discussed: (1) facilitating partnerships across departments, roles, and levels;(2) level setting and arriving at a common level of understanding among team members; (3) obtaining buy-in from relevant stakeholders and data stewards; (4) designing for the needs of the specific institutional context; (5) utilizing national and international standards whenever possible; and (6) understanding the institutional landscape of learning analytics and designing datasets accordingly.

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“…As for the supplementary sample, several interesting assistance works correspond to: a customized course advising to determine the optimal term‐by‐term course selections for students based on their incoming traits and previous course history and performance (Nam, Lonn, Brown, Cinda‐Sue, & Darryl, ), a design research process for a manipulative‐based fractions intervention program (Mendiburo, Sulcer, & Hasselbring, ), using learner promotions of content to identify quality content and implications for students and instructors (Gunnarsson & Alterman, ), time management (Ferguson & Clow, ; Jo, Kim, & Yoon, ; Kovanovic et al, ; Miyamoto et al, ; Tabuenca et al, ; You, ), and curriculum design (Dunbar, Dingel, & Prat‐Resina, ; Méndez, Ochoa, Chiluiza, & de Wever, ; Monroy, Snodgrass, & Whitaker, ).…”

Section: Applicationsmentioning

confidence: 99%

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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Before the emergence of computer‐based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014–2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state‐of‐the‐art and an acumens vision to inspire future labor. This article is categorized under: Application Areas > Education and Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

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Customized course advising

Cited by 20 publications

References 9 publications

What Makes a Grade? Harnessing Demographic and Supportive Factors to Predict General Chemistry I Completion

What Makes a Grade? Harnessing Demographic and Supportive Factors to Predict General Chemistry I Completion

Rearchitecting Data for Researchers: A Collaborative Model for Enabling Institutional Learning Analytics in Higher Education

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

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