Supporting the Literacy Skills Necessary to Master Technical Vocabulary

Background: Career and Technical Education programs integrate authentic experiences that incorporate work-related skills into curricula, which provide students instruction related to a wide variety of careers and occupational fields. Pretest, intervention assessments, and posttest curricular initiatives are common strategies that ensure student success. These strategies often include interventions for Microsoft Excel, which students have difficulty understanding due to the sophisticated level of the technical vocabulary required to comprehend discipline-specific verbiage. Having a clear understanding of technical vocabulary will enable them to obtain an industry-based certification. Purpose: The purpose of this mixed-methods study was to determine how implementing an intervention for technical vocabulary in a dual credit Career and Technical Education business course might affect students’ ability to pass posttest exams. This study evaluated the implementation of the pretest, intervention assessment, and posttest model in a dual credit Business Computer Applications class that taught Microsoft Office applications to students. The intervention was introduced based on student needs and addressed misconceptions about the meaning of the technical vocabulary in the form of verbal discussion, and a daily digital word wall. The study addressed the following question: In order to ensure success for students who have participated in Career and Technical Education industry-based training, how will we support the literacy skills necessary to master technical vocabulary to the extent that students are successful in passing their posttest assessments? Methods: A mixed-method design to collect data and to explain the appropriateness of the methodology was used to interpret answers to the research question. This methodology combined both qualitative and quantitative research, which assisted in neutralizing the weaknesses associated with a singular approach to the study. Additionally, a convergent parallel mixed-method research design was most beneficial, as it merged the qualitative and quantitative data in a triangulation design to ensure the research question was addressed thoroughly. The archival data sets were compared in a paired sample t-test for pretest (M=19.08, SD=14.093) and posttest (M=87.13, SD=8.42). The control group scores from the paired sample t-test were (M=21.08, SD=12.301) and posttest (M=74.64, SD=14.985) and were received from a high school dual credit course in which the intervention was applied. The quantitative data sets evaluated by the researcher, included Excel pretests, vocabulary intervention assessments, and end of the unit posttest. A control group was established by retrieving data from a previous semester course in which the students did not receive the intervention. The data from the control group and the experimental group were evaluated via a t-test between groups on pretest and posttest scores to determine the effectiveness of the intervention. Students in the intervention group were provided with an anonymous end of course survey, which was completed as a part of their regular end of semester activities. This survey asked open-ended questions about the effectiveness of the intervention and the method of instruction. Additionally, it was utilized as the qualitative data set and a portion of the quantitative data set for the study. Qualitative content analysis was used to evaluate the students’ written answers, which were grouped into themes based on the comments made. Coded themes were the perception of the method of instruction, and their views of the relevancy of the content. The prewritten questions were evaluated in percentages based on student responses. The researcher utilized a second reviewer to reduce the likelihood of biases and blind spots by reading the answers independently, developed themes, and compared ideas. Results: A paired-samples t-test was conducted to compare the students’ pretest Excel scores to their posttest Excel scores. There was a strongly significant difference (p= .001 ) in the scores for pretest (M=19.08, SD=14.093) and posttest (M=87.13, SD=8.42). versus scores in the control group scores (pretest, M=21.08, SD=12.301; posttest, M=74.64, SD=14.985). Results of the end-of-course survey indicated that student perceptions of the intervention were positive. The reporting method was a score of 1 to 5, and the result was (M=4.57, SD=0.60). Additionally, the students believed that the course developed their ability to think critically about their subject resulting in (M=4.52, SD=0.81). The survey also indicated that students perceived the learning was relevant to their field of study, career, or degree at (M=4.90, SD=0.30) while providing meaningful feedback at (M=4.81, SD=0.51). Conclusion: These results suggest that relevant technical vocabulary intervention can influence student success.