@phdthesis{walker_learning_2016,
	address = {United States -- Minnesota},
	type = {D.{Ed}.},
	title = {Learning analytics in post-secondary education: {The} utilization of big data to support part-time adjunct faculty members in the academic advisement of nontraditional students},
	copyright = {Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.},
	shorttitle = {Learning analytics in post-secondary education},
	url = {https://search.proquest.com/eric/docview/1826017702/abstract/D691669D3D71409CPQ/12},
	abstract = {This research study evaluated the use of learning analytics and big data to support adjunct faculty members in the academic advising of non-traditional students in a post-secondary educational setting, as a critical issue in educational leadership and management. This study sought to evaluate the espoused theories and theories-in- use with analytical tools by the participants in the School of Study (SOS) where the action research study took place. The evaluation was conducted through an intervention consisting of the administration of pre- and post-intervention data collection instruments (a survey instrument and semi-structured interview questions). This study specifically examined the new analytical tools implemented by the college to discern the challenges faced by the adjunct faculty members in leading, managing, and being held accountable and responsible for the academic advising of non-traditional students. This study looked to identify how the implementation of new technologies (Learning Management System [LMS] and Student Success System [S3]) and could help to improve academic advising practices. This action research study utilized a mixed methods model (sequential explanatory design) to analyze: (a) a pre- and post-intervention survey instrument , (b) open-ended, semi-structured, pre- and post-intervention interview questions, (c) minutes from two faculty meetings and two in-service activities, and (d) data reviewed from the LMS and S3 systems. The results from the open-ended interview questions were used to support the findings of the survey instrument to ascertain where the knowledge of the academic advising practices was generated and if that knowledge was deemed by the adjunct faculty members to be adequate to allow them to effectively advise non-traditional students.},
	language = {English},
	urldate = {2019-01-10},
	school = {Capella University},
	author = {Walker, Nathaniel B.},
	year = {2016},
	keywords = {Adult learners, Education, Learning analytics, Learning management systems, Non-traditional students},
}

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B."],"bibdata":{"bibtype":"phdthesis","type":"D.Ed.","address":"United States – Minnesota","title":"Learning analytics in post-secondary education: The utilization of big data to support part-time adjunct faculty members in the academic advisement of nontraditional students","copyright":"Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.","shorttitle":"Learning analytics in post-secondary education","url":"https://search.proquest.com/eric/docview/1826017702/abstract/D691669D3D71409CPQ/12","abstract":"This research study evaluated the use of learning analytics and big data to support adjunct faculty members in the academic advising of non-traditional students in a post-secondary educational setting, as a critical issue in educational leadership and management. This study sought to evaluate the espoused theories and theories-in- use with analytical tools by the participants in the School of Study (SOS) where the action research study took place. The evaluation was conducted through an intervention consisting of the administration of pre- and post-intervention data collection instruments (a survey instrument and semi-structured interview questions). This study specifically examined the new analytical tools implemented by the college to discern the challenges faced by the adjunct faculty members in leading, managing, and being held accountable and responsible for the academic advising of non-traditional students. This study looked to identify how the implementation of new technologies (Learning Management System [LMS] and Student Success System [S3]) and could help to improve academic advising practices. 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This action research study utilized a mixed methods model (sequential explanatory design) to analyze: (a) a pre- and post-intervention survey instrument , (b) open-ended, semi-structured, pre- and post-intervention interview questions, (c) minutes from two faculty meetings and two in-service activities, and (d) data reviewed from the LMS and S3 systems. The results from the open-ended interview questions were used to support the findings of the survey instrument to ascertain where the knowledge of the academic advising practices was generated and if that knowledge was deemed by the adjunct faculty members to be adequate to allow them to effectively advise non-traditional students.},\n\tlanguage = {English},\n\turldate = {2019-01-10},\n\tschool = {Capella University},\n\tauthor = {Walker, Nathaniel B.},\n\tyear = {2016},\n\tkeywords = {Adult learners, Education, Learning analytics, Learning management systems, Non-traditional students},\n}\n\n\n\n","author_short":["Walker, N. 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