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- All Subjects: Adult Education
- Creators: Dorn, Sherman
Description
This study investigated the impact of learning about cultural intelligence (CQ) from senior U.S. Army Special Forces leaders (Group Commanders and Group Command Sergeants Major) on aspiring Special Forces Captains (students) at the Captains Career Course. Three research questions addressed the influence of senior leader interventions on students’ CQ scores, motivation to work with partner forces, and intentions to improve CQ. The study involved quantitative and qualitative data for each of the three comparison groups: control, face-to-face (in-person interaction with senior leaders), and podcast (audio-only recordings). The quantitative data measured CQ capabilities of motivation, cognition, metacognition, and behavior. Descriptive statistics revealed that from the pre-test to the post-test, the control and podcast groups experienced increased self-assessment scores on all four constructs but decreased observer assessment scores. By contrast, the face-to-face group experienced both a decrease in observer assessment scores as well as a marginal decrease in self-assessment scores (on motivation and metacognition).
Exploring motivation to work with partner forces, analysis of the group interview transcripts revealed that the control group attributed their motivation primarily to their prior experiences, while participants in the face-to-face group reported mixed feelings regarding prior experiences but highlighted the impact of senior Special Forces leaders' stories on their motivation. The podcast group credited their course experience and the senior leaders' narratives for their increased motivation.
Examining the influence of senior leader stories on intent to improve CQ, the control group provided generic responses focused on improving cognition. The face-to-face group offered more specific, action-oriented answers emphasizing business systems, sociolinguistics, and cultural values. The podcast group produced varying responses, with some sharing basic intent and others detailing specific strategies such as language fluency and cultural immersion. Participants across all three groups expressed a strong intention to seek out mentorship and stories from experienced individuals.
In conclusion, this study highlights the myriad influences on aspiring Special Forces Captains' CQ and the multifaceted impact of senior Special Forces leaders' stories. The narratives contributed to increased motivation, deeper understanding of the Special Forces mission, and specific strategies for improving CQ, providing valuable insights for military education and training programs.
ContributorsKohistany, Mahboba Lyla (Author) / Dorn, Sherman (Thesis advisor) / Livermore, David (Committee member) / Amrein-Beardsley, Audrey (Committee member) / Arizona State University (Publisher)
Created2023
Educational Data Mining: An Application of a Predictive Model of Online Student Enrollment Decisions
Description
College and university enrollment has decreased nationwide every year for more than a decade as educational consumers increasingly question the value of higher education and discover alternatives to the traditional university system. Enrollment professionals seeking growth are tasked to develop and implement innovative solutions to address increasing enrollment challenges by being responsive to consumer values, interests, and needs. This multi-phase mixed methods action research study explores educational data mining and machine learning to understand and predict the enrollment decisions of admitted applicants (n=3,843) at the online campus of a public research university (phase one). Then, this innovation is distributed to understand how university enrollment professionals (n=7) interpret and are affected by the factors that influence online student enrollment decisions (phase two). Logistic regression was used to evaluate 24 independent variables to classify each applicant into a dichotomous dependent outcome: will an applicant enroll or will they not. The model identified 10 significant predictors and accurately categorized 81% of the enrollment outcomes at its peak. The population was comprised of online adult learners and the findings were carefully compared to the findings of previous studies which differed in institutional settings (on campus) and student populations (first-year students). Additionally, the study aimed to extend the work of previous literature through a second application phase within the local context. The second phase was guided by distributed leadership theory and the four-stage theory of organizational change and introduced the model to enrollment professionals within the local context through participation in a workshop coupled with a pre-/post-workshop survey. This convergent parallel mixed methods design resulted in themes that demonstrated enrollment managers had a genuine desire to understand and apply the model to assist in solving complex enrollment challenges and were interested in using the model to inform their perspectives, decision-making, and strategy development. This study concludes that educational data mining and machine learning can be used to predict the enrollment decisions of online adult students and that enrollment managers can use the data to inform the many enrollment challenges they are tasked to overcome.
ContributorsSinger, Cody Gene (Author) / Ross, Lydia (Thesis advisor) / Dorn, Sherman (Thesis advisor) / Cillay, David (Committee member) / Arizona State University (Publisher)
Created2023