Predicting student success with and without library instruction using supervised machine learning methods

被引:0
|
作者
Harker, Karen [1 ]
Hargis, Carol [1 ]
Rowe, Jennifer [1 ]
机构
[1] Univ North Texas, UNT Lib, Denton, TX 76205 USA
关键词
One shot sessions; Library research instruction; Student success; Predictive modeling; SAT SCORES; IMPACT;
D O I
10.1108/PMM-12-2023-0047
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
PurposeThe main purpose of this analysis was to demonstrate the value of predictive modeling of student success and identify the key groups of students for which library instruction could provide the most impact.Design/methodology/approachData regarding the attendance of library instruction associated with a first-year writing course were combined with student demographic and academic data over a four year period representing over 10,000 students. We applied supervised machine learning methods to determine the most accurate model for predicting student outcomes, including course outcome, persistence and graduation. We also assessed the impact of library instruction on these outcomes.FindingsThe gradient-boosted decision tree model provided the most accurate predictions. The impact of library instruction was modest but still was second only to the previous grade point average (GPA). The value of this metric, however, was greatest for students who were struggling, especially those who were first-generation students, regardless of ethnicity. More notably, the impact of library instruction was substantially greater for specific student demographics, including students with lower cumulative GPAs.Research limitations/implicationsFeatures of the models were limited to high-level academic metrics, some of which may not be very useful in predicting outcomes. Measures more closely related to learning styles, the course or course of study could provide for greater accuracy.Practical implicationsPrediction modeling could allow for a more selective approach to outreach and offers information that the librarian can use to customize instruction sessions and reference interactions.Social implicationsTargeting students who may be at risk of not succeeding in a course has ethical implications either way. If used to bias the subjective assessments, these predictions could produce self-fulfilling prophecies. Conversely, to ignore indicators of possible difficulties the student may have with the material is a disservice to the education of that student.Originality/valueThere are few studies that have incorporated library instruction into models of predicting student outcomes. Library resources and services can play a major role in the success of students, particularly those who have had less exposure to the resources and skills needed to use these resources.
引用
收藏
页码:77 / 90
页数:14
相关论文
共 50 条
  • [41] Predicting corporate policies using downside risk: A machine learning approach
    Avramov, Doron
    Li, Minwen
    Wang, Hao
    JOURNAL OF EMPIRICAL FINANCE, 2021, 63 : 1 - 26
  • [42] Predicting User Behavior in e-Commerce Using Machine Learning
    Ketipov, Rumen
    Angelova, Vera
    Doukovska, Lyubka
    Schnalle, Roman
    CYBERNETICS AND INFORMATION TECHNOLOGIES, 2023, 23 (03) : 89 - 101
  • [43] Predicting future hospital antimicrobial resistance prevalence using machine learning
    Vihta, Karina-Doris
    Pritchard, Emma
    Pouwels, Koen B.
    Hopkins, Susan
    Guy, Rebecca L.
    Henderson, Katherine
    Chudasama, Dimple
    Hope, Russell
    Muller-Pebody, Berit
    Walker, Ann Sarah
    Clifton, David
    Eyre, David W.
    COMMUNICATIONS MEDICINE, 2024, 4 (01):
  • [44] MOBILE LEARNING ANALYTICS APPLICATION: USING STUDENTS' BIG DATA TO IMPROVE STUDENT SUCCESS
    Gaftandzhieva, Silvia
    Doneva, Rositsa
    Petrov, Svetoslav
    Totkov, George
    INTERNATIONAL JOURNAL ON INFORMATION TECHNOLOGIES AND SECURITY, 2018, 10 (03): : 53 - 64
  • [45] Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning
    Eckardt, Jan-Niklas
    Rillig, Christoph
    Metzeler, Klaus
    Kramer, Michael
    Stasik, Sebastian
    Georgi, Julia-Annabell
    Heisig, Peter
    Spiekermann, Karsten
    Krug, Utz
    Braess, Jan
    Girlich, Dennis
    Sauerland, Cristina M.
    Woermann, Bernhard
    Herold, Tobias
    Berdel, Wolfgang E.
    Hiddemann, Wolfgang
    Kroschinsky, Frank
    Schetelig, Johannes
    Platzbecker, Uwe
    Mueller-Tidow, Carsten
    Sauer, Tim
    Serve, Hubert
    Baldus, Claudia
    Schaefer-Eckart, Kerstin
    Kaufmann, Martin
    Krause, Stefan
    Haenel, Mathias
    Schliemann, Christoph
    Hanoun, Maher
    Thiede, Christian
    Bornhaeuser, Martin
    Wendt, Karsten
    Middeke, Jan Moritz
    HAEMATOLOGICA, 2023, 108 (03) : 690 - 704
  • [46] A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine Learning
    Wongvorachan, Tarid
    Bulut, Okan
    Liu, Joyce Xinle
    Mazzullo, Elisabetta
    INFORMATION, 2024, 15 (06)
  • [47] Estimating the Prevalence of Dementia in India Using a Semi-Supervised Machine Learning Approach
    Jin, Haomiao
    Crimmins, Eileen
    Langa, Kenneth M.
    Dey, A. B.
    Lee, Jinkook
    NEUROEPIDEMIOLOGY, 2023, 57 (01) : 43 - 50
  • [48] Predicting student success: A 10-year review using integrative review and meta-analysis
    Campbell, AR
    Dickson, CJ
    JOURNAL OF PROFESSIONAL NURSING, 1996, 12 (01) : 47 - 59
  • [49] Forecasting Student Outcomes at University-Wide Scale Using Machine Learning
    Wham, Drew
    SEVENTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE (LAK'17), 2017, : 576 - 577
  • [50] Railway defect detection based on track geometry using supervised and unsupervised machine learning
    Sresakoolchai, Jessada
    Kaewunruen, Sakdirat
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2022, 21 (04): : 1757 - 1767