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Dressler Makes Things (DMT), as a thesis project, explores the viability of DMT as a monetized blog. DMT is a life improvement blog with a focus on cooking. Its blog niche sits between nostalgic, sensory food blogs and professional, actionable life improvement blogs. Because blogs that bridge this gap are

Dressler Makes Things (DMT), as a thesis project, explores the viability of DMT as a monetized blog. DMT is a life improvement blog with a focus on cooking. Its blog niche sits between nostalgic, sensory food blogs and professional, actionable life improvement blogs. Because blogs that bridge this gap are rare, DMT aims to fill this need by making cooking easy, affordable, and attainable for anyone, but particularly females in their early twenties. This thesis explores marketing topics like brand, blog niche, unique value proposition, current viewers, and user personas, as well as information about the current blogging environment and an analysis of relevant competitors. It also develops marketing objectives for DMT, as well as positioning, conversion, referral, content promotion, and partnership strategies to reach these goals. Finally, it discusses distribution, pricing, and promotional tactics, as well as an operational plan and financial projections.
ContributorsParsons, Dressler Eileen (Author) / Gray, Nancy (Thesis director) / Bitter, Gary (Committee member) / School of Art (Contributor) / Department of Marketing (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
The focus of this project is developing a business plan for faith-based counseling for college students. Renewed Living Counseling Center (RLCC) is a faith-based counseling center in the Tempe area serving Arizona State University students. RLCC strives to bring healing and wholeness to each student who comes through the doors,

The focus of this project is developing a business plan for faith-based counseling for college students. Renewed Living Counseling Center (RLCC) is a faith-based counseling center in the Tempe area serving Arizona State University students. RLCC strives to bring healing and wholeness to each student who comes through the doors, to empower them to realize and live out their potential, by providing them with the skills to accomplish their dreams and live full lives, through counseling, motivation, education, and treating studentʼs behaviors to become whole and successful. Research indicates that the proposed center, Renewed Living Counseling Center (RLCC), has great potential for success because:

1. Spirituality and faith are increasingly recognized as important aspects in a personʼs life. National research shows that 66% of people feel counseling should include spirituality. Research with ASU students found that students reflect this statistic, as they feel spirituality is an important part of counseling. Students also feel spirituality is appropriate to include as part of counseling services offered by centers referred to by ASU.

2. There is a need for counseling at ASU. Nationally,approximately1,100 college students commit suicide each year. At ASU, almost one-third of students reported feeling so depressed that it is difficult to function, and 0.9% report having attempted suicide within the past year.

3. Surveys of ASU students indicate that students who describe themselves as being religious are more desirous that counseling include a spiritual dimension. Surveys of campus pastors indicate that over 80% believe there is a need for faith-based counseling and would refer students to a local center.

4. Price is an issue. Indeed, a survey of campus pastors indicated that they believed cost of counseling to be one of the primary deterrents to students seeking help. One way to control costs is to use a mixture of residents and licensed counselors. As in medicine, students must complete coursework along with a period of residency or internship to obtain licensing. Both religious and secular masters programs in counseling exist in the greater Phoenix area. Thus, there is a potential supply of students who could work as residents, permitting RLCC to offer counseling services at reasonable prices.
ContributorsMatthews, Rachel Leigh (Author) / Steinbart, Paul (Thesis director) / Chung, Sally (Committee member) / Sanders, Ben (Committee member) / Barrett, The Honors College (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor)
Created2014-05
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Description
While predicting completion in Massive Open Online Courses (MOOCs) has been an active area of research in recent years, predicting completion in self-paced MOOCS, the fastest growing segment of open online courses, has largely been ignored. Using learning analytics and educational data mining techniques, this study examined data generated by

While predicting completion in Massive Open Online Courses (MOOCs) has been an active area of research in recent years, predicting completion in self-paced MOOCS, the fastest growing segment of open online courses, has largely been ignored. Using learning analytics and educational data mining techniques, this study examined data generated by over 4,600 individuals working in a self-paced, open enrollment college algebra MOOC over a period of eight months.

Although just 4% of these students completed the course, models were developed that could predict correctly nearly 80% of the time which students would complete the course and which would not, based on each student’s first day of work in the online course. Logistic regression was used as the primary tool to predict completion and focused on variables associated with self-regulated learning (SRL) and demographic variables available from survey information gathered as students begin edX courses (the MOOC platform employed).

The strongest SRL predictor was the amount of time students spent in the course on their first day. The number of math skills obtained the first day and the pace at which these skills were gained were also predictors, although pace was negatively correlated with completion. Prediction models using only SRL data obtained on the first day in the course correctly predicted course completion 70% of the time, whereas models based on first-day SRL and demographic data made correct predictions 79% of the time.
ContributorsCunningham, James Allan (Author) / Bitter, Gary (Thesis advisor) / Barber, Rebecca (Committee member) / Douglas, Ian (Committee member) / Arizona State University (Publisher)
Created2017