**Model for Blended Supervision of Post-Graduate Students**

Mildred Atieno Ayere

[18] Mtebe, J.S., & Raisamo, R. A model for assessing learning management system suc‐ cess in higher education in Sub-Saharan countries. The Electronic Journal of Informa‐

[19] Ives, B., Olson, M., & Baroudi, J.J. The measurement of user information satisfaction.

[20] DeVon, H.A., Block, M.E., Moyle-Wright, P., Ernst, D.M., Hayden, S.J., Lazzara, D.J. et al. A psychometric toolbox for testing validity and reliability. Journal of Nursing

[21] Goodwin L.D., & Leech, N.L. Understanding correlation: Factors that affect the size

[22] Abrahams, D. Technology adoption in higher education: A framework for identify‐ ing and prioritising issues and barriers to adoption of instructional technology. Jour‐

[23] Al-Busaidi, K., & Al-Shihi, H. Key factors to instructors' satisfaction of learning man‐ agement systems in blended learning. Journal of Computing in Higher Education.

[24] Liaw, S., Huang, H., & Chen, G. Surveying instructor and learner attitudes toward e-

[25] Naveh, G., Tubin, D., & Pliskin, N. Student LMS use and satisfaction in academic in‐ stitutions: The organisational perspective. Internet and Higher Education. 2010; 13:

[26] Sun, P., Tsai, R., Finger, G., Chen, Y., & Yeh, D. What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction.

[27] Fariha, Z. & Zuriyati, A. Comparing Moodle and eFront software for learning man‐ agement system. Australian Journal of Basic and Applied Sciences. Special Issue

of *r*. The Journal of Experimental Education. 2006; 74 (3): 251–266.

nal of Applied Research in Higher Education. 2010; 2 (2): 34–49.

learning. Computers & Education. 2008; 49: 1066–1080.

Computers & Education. 2008; 50: 1183–1202.

tion Systems in Developing Countries. 2014; 61 (7): 1–17.

Communications of the ACM. 1983; 26: 785–793.

378 E-Learning - Instructional Design, Organizational Strategy and Management

Scholarship. 2007; 39 (2): 155–164.

2012; 24: 18–39.

127–133.

2014; 8 (4): 158–162.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/60656

#### **Abstract**

Supervision of eLearning students at Maseno University poses a great challenge to the normal institutional order because most senior lecturers qualified for postgraduate super‐ vision are technologically illiterate, semi-literate, or challenged [10]. The recommended lecturer to student ratio for postgraduate supervision in Maseno University is 1:5 and 1:3 for master's and PhD students, respectively, but the actual ratio is 1:12 [23]. The challenge of high student numbers in three different campuses, low numbers of qualified supervi‐ sors; and fully online students is a big problem. ELearning is not new to the developed world but a fairly new concept in Africa [2, 6, 7]. Through eLearning, Maseno is fulfilling the global demand for universal lifelong learning [26]. Introducing blended supervision was a strategy seeking to harness the opportunities in the online platform by reducing distance between students while increasing the rate and quality of feedback [8, 21, 31]; leveraging the affordances of virtual learning to create an interactive environment for learners and faculty [11, 18, 15]. Objectives of this project were to develop policy and pro‐ cedures for online supervision, Identify postgraduate supervision milestones, and Build a collaborative research environment. The study used the critical case study design [28] and was hinged on constructivist theory [15]. The population consisted of 513 students, 42 lecturers from the 5 schools with postgraduate courses at eCampus, and 8 university administrators. Purposive sampling led to 149 students, 11 lecturers, and 3 administrators from one school that fully embraced the model. Data were collected using online discus‐ sions, observations, and interviews. Data were analyzed using time series analysis to identify milestones in the supervision process while predicting best interaction models for online supervision. Regression logic model further helped predict expected comple‐ tion rates based on existing supervisor to student ratios. The study identified key super‐ vision milestones as assistance in drafting an acceptable concept paper and proposal, quality interaction and feedback from supervisor, provision of adequate tools to support research processes, identification with a collaborative research team, and exposure to re‐ search seminars and presentations. From the milestones, the study school identified a group of qualified supervisors and offered them training on use of the online platform and resources in supervision. This study concluded that a pilot model for blended post‐ graduate supervision is in its formative stages, the collaborative postgraduate research course area is being piloted in six schools, online supervision has enabled most schools to

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share supervisors with other research institutions at no extra cost, predicted completion rate for postgraduate research is one year on the blended model, and research outputs from post-graduate students have increased by 50% on average. It is recommended that results from this study need to be replicated in other schools before it can fully inform university wide policy, making it a continuing work in progress.

**Keywords:** eLearning, online supervision, eCampus, postgraduate research, blended su‐ pervision
