Problem and Purpose Statement

Problem and Purpose Statement

Doctoral scholar attrition is presently a key concern confronted by colleges across the US, comprising counselor education and its undesirable impacts are common (Schendel & McCowan, 2016). Doctoral scholar attrition damagingly impacts several areas like;

University Due to the decreased enrollment of students, the university gets a smaller amount tuition fee. It is cheaper and easier to retain doctoral scholars than to follow fresh ones (Astin, 1993). Instead, colleges are compelled to expense expenses and resources (e.g., campus visits, procurement of materials, evaluating applications) and pursuing fresh scholars (Aulck, Velagapudi, Joshua, & Jevin, 2016).

Student Since getting a doctorate  is an expensive and lengthy effort. Scholars who drop out are possible to get a copious total of amount overdue without the compromise of a new money-spinning career (Thomas, 2002). Moreover, these doctoral scholars characteristically experience an emotional fee, for example, shattered intellects of self-respect and depression after pulling out of the degree (Crawford, Randolph, & Rosenstein, 2016).

These concerns associated with different parties affecting doctoral scholars’ attrition rate demonstrated that there is a requirement for a decent mechanism that requires to be adopted and developed by higher education institutions in the US to help overcome the concern of scholar attrition to the increase of number of students dropped out (Willis & Carmichael, 2011). Therefore, this research mainly aims to investigate the effectiveness of machine learning and AI-based predictive models for holistic prediction of scholar attrition in these institutions (Braxton & Lien, 2000). It likewise gets to the core of the knowledge and experiences of doctoral scholars withdrawing from their Ph.D. programs across the nation (Craddock, Birnbaum, Rodriguez, Cobb, & Zeeh, 2011).

Research Questions

The proposed dissertation research utilizes “attribution theory” to consider the following research questions:

1. What made undergraduate scholars willingly withdraw from doctoral degree programs in higher educational institutions in the US? (Gardner, 2008)
2. What are the significant machine learning techniques used to predict doctoral students’ retention significantly? (Gardner, 2008)
3. Is any there is any gender variation in drop out?
4. Role of Cumulative GPA in drop out scenarios?
5. Is any financial assistance plays a crucial role in drop out?

6. Is any ethnicity difference in dropouts

7. Are there any factors associated with institutions like institution ranking, location, and other academic characteristics

8. Any personal factors like marriage, kids associated with dropouts.

9. Which Machine Learning model gives best prediction in terms of accuracy

                  Research Method

Quantitative Method

Requirements –

1. 1)         APA style
2. 2)         Scholarly references
3. 3)         In-text citations for every argument.