scholarly journals Mathematics Course Placement Using Holistic Measures: Possibilities for Community College Students

2018 ◽  
Vol 120 (2) ◽  
pp. 1-42
Author(s):  
Federick Ngo ◽  
W. Edward Chi ◽  
Elizabeth So Yun Park

Background/Context Most community colleges across the country use a placement test to determine students’ readiness for college-level coursework, yet these tests are admittedly imperfect instruments. Researchers have documented significant problems stemming from overreliance on placement testing, including placement error and misdiagnosis of remediation needs. They have also described significant consequences of misplacement, which can hinder the educational progression and attainment of community college students. Purpose/Objective/Research Question We explore possibilities for placing community college students in mathematics courses using a holistic approach that considers measures beyond placement test scores. This includes academic background measures, such as high school GPA and math courses taken, and indicators of noncognitive constructs, such as motivation, time use, and social support. Setting The study draws upon administrative data from a large urban community college district in California that serves over 100,000 students each semester. The data enable us to link students’ placement testing results, survey data, background information, and transcript records. Research Design We first use the supplemental survey data gathered during routine placement testing to conduct predictive exercises that identify severe placement errors under existing placement practices. We then move beyond prediction and evaluate student outcomes in two colleges where noncognitive indicators were directly factored into placement algorithms. Findings/Results Using high school background information and noncognitive indicators to predict success reveals as many as one quarter of students may be misassigned to their math courses by status quo practices. In our subsequent analysis we find that students placed under a holistic approach that considered noncognitive indicators in addition to placement test scores performed no differently from higher scoring peers in the same course. Conclusions/Recommendations The findings suggest a holistic approach to mathematics course placement may improve placement accuracy and provide access to higher level mathematics courses for community college students without compromising their likelihood of success.

Author(s):  
Liza N. Meredith ◽  
Patricia A. Frazier ◽  
Jacob A. Paulsen ◽  
Christiaan S. Greer ◽  
Kelli G. Howard ◽  
...  

2020 ◽  
Vol 8 (1) ◽  
pp. 4-17
Author(s):  
Nor Akmal Mohamad ◽  
Madihah Khalid

Building information modelling (BIM) is one of the new technologies being used in architectural and constructions projects. At present, BIM curricula are being taught in many Malaysian higher learning institutions, including at the certificate level in community colleges. Even though many studies have investigated behavioural intention to adopt BIM in the industrial setting, studies on the intention to use BIM among students during their training or learning have not received the same level of attention. This study, therefore, investigated the extent to which community college students are willing to accept and use BIM. Factors that influenced their behavioural intention to use BIM, as well as the relationship between the factors and intention to use were also examined. The Technology Acceptance Model (TAM) was used as the theoretical framework to guide the research, where students’ behavioural intention to use BIM was explained through their perceptions of its usefulness and ease of use, as well as their attitude towards BIM utilization in the classroom. A total of 144 community college students enrolled in the architecture programmes in Malaysia were selected as the sample using convenience sampling. The findings show that the students’ behavioural intention to adopt BIM is high. They also perceive BIM as useful and easy to use, and their attitude towards BIM usage appears to be positive. The regression model produced an adjusted R-squared value of 0.790 indicating that 79% of the total variance in the students’ intention to use BIM can be explained by the three independent variables, i.e., perceived usefulness, ease of use, and attitude. Keywords: Building information modelling, perceived usefulness, perceived ease of use, attitude, intention to use, behavioural intention, Technology Acceptance Model


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