Using cluster analysis to characterize meaningful learning in a first-year university chemistry laboratory course

Abstract

The Meaningful Learning in the Laboratory Instrument (MLLI) was designed to measure students' cognitive and affective learning in the university chemistry laboratory. The MLLI was administered at the beginning and the end of the first semester to first-year university chemistry students to measure their expectations and experiences for learning in their laboratory course. To better understand what students' expectations for learning were fulfilled, and what expectations went unmet, cluster analysis was used to examine students' expectations and experiences as measured by their average scores on the MLLI at both the beginning and the end of the semester. The cluster solution yielded four clusters.Three of the four clusters consisted of parallel responses, i.e., the first cluster had both low expectations and low experiences, the third cluster had both high expectations and high experiences, and the second cluster was in the middle of the first and third. The fourth cluster had expectations similar to those of the high cluster, but surprisingly had experiences similar to those of the middle and low clusters.Descriptions of each cluster of students, including distinguishing characteristics of their learning in the laboratory, and the implications of this research are discussed.

Lernzuwachs in Blended‐Learning Laborpraktika transparent machen – Feedbackfunktionen des LMS erweitern

“…ganzeExperimente,k çnnenmittelselektronischerSimulation absolviert werden [3]. Chemiestudierendez eigend adurch nichtn ur bessereL eistungen [ 4],s ondern fühlen sich durch daszusätzlich mçgliche Online-Feedbackbesserauf diePraktikumszeit vorbereitet [ 5,6].A uchd ie elektronischen Aufgabeni ne inem online Lernangebotw erdenv on Studierenden alse ineg roße Unterstützungi mL ernprozess angesehen [ 7]. Jedochist einp ositiver Einfluss aufdas Lernverhaltenabhängigv on derK ombination ausA ufgabenz ur Selbstkontrolle unde inem entsprechendenu nterstützendenF eedback, das Studierendeb eims elbständigenA rbeitene rhalten [ 8].D ie online-Rückmeldungz ue lektronischenA ufgabeni st bisher durchverschiedene Va riantendes Feedbacks, dieinLern-Management-Systemen( LMS) bereitsf esti mplementiert sind, mçglich.…”

Faculty and student goals for undergraduate laboratory

“…[12][13][14][15][16] The findings of a national study point toward emphasizing the affective domain specifically with emphasis on developing a "positive self-concept as a student of chemistry." Their work emphasizes the importance of allowing students to make decisions in laboratory whether it be about experimental design, analysis, interpretation, or communication.…”

“…Their work emphasizes the importance of allowing students to make decisions in laboratory whether it be about experimental design, analysis, interpretation, or communication. 7,[12][13][14][15][16] …”

“…6 To address the gaps in the research that has been carried out in laboratory in 2005 we embarked with our collaborators on a research trajectory that would help us understand the goals that faculty and students have for laboratory. [7][8][9][10][11][12][13][14][15][16] The ultimate goal has been to describe the correspondence between the two and to shape and guide the laboratory curriculum in ways that would bring about a better correspondence.…”

Utilizing Cluster Analysis of Close-Ended Survey Responses to Select Participants for Qualitative Data Collection

“…This technique has been used within other disciplines to group different types of data and entities of systems, such as chemicals (Maccuish & Maccuish, 2014), manufacturing decisions (Lorentz, Hilmola, Malmsten, & Srai, 2016), or planets (Jiang, Yeh, Hung, & Yang, 2006), based on a series of factors or variables. In engineering education, cluster analysis has been used to group participants who have similar attributes such as epistemic beliefs (Faber, Vargas, & Benson, n.d.), activities within a learning environment (Antonenko, Toy, & Niederhauser, 2012;Galloway & Bretz, 2015a, 2015b, relative risk of attrition (Chan & Bauer, 2014), or who exhibit certain behaviors in courses (Karabenick, 2003;Raker et al, 2015;Shell & Soh, 2013;Stewart, Miller, Audo, & Stewart, 2012). Cluster analysis can help researchers who are using a mixed methods approach select participants for interviews when a variation of participant attributes or perspectives is desired.…”