Research Proposal Instructions

During this course, you have developed a purpose statement and a corresponding research question, evaluated quantitative and qualitative research studies, identified a research design that you could use to address your research question, and composed a Literature Review on your topic.

As a culminating project for this course, you will incorporate your previous work into a 25-slide PowerPoint presentation Research Proposal. You are not required to conduct the researchthat you discuss in your proposal; instead, outline a possible research plan. In your presentation, include the following elements:

• Title (1 slide)

• Abstract (1 slide)

• Purpose of the study (1 slide)

• Research question(s) (and hypotheses if selecting a quantitative design) (1–2 slides)

• Definition of variables and key terms (4 slides maximum)

• Operational definitions (i.e., measurable definitions) of variables

• Review of the literature (5 slides maximum)

• Summary of the significant themes that emerged in your literature review (# of slides as needed)

• Research design (1 slide)

• Sampling (1 slide)

• Methods of data collection (1–2 slides)

• Data-analysis procedures (2–4 slides)

• Ethics and human relations (1–2 slides)

• Timeline (1 slide)

• Reference list (# of slides as needed)

Follow correct APA format for citations as necessary. See the Research Proposal Grading Rubric for the specific grading criteria.

Submit this assignment by 11:59 p.m. (ET) on Friday of Module/Week 8.

Literature Review Assignment

Review due date: 1 May, 2017.

Selection of topic/area/theme of review by: 20 March, 2017.

Submission instructions: Submit via moodle.

You are asked to write a Literature Review on a specific topic within one area of a Computer Science specialization that you are interested in (see example below on topic/CS area). Your review will be written in the form of a research paper according to the IEEE conference style format which can be found in: http://www.ieee.org/conferences_events/conferences/publishing/templates.html for Word and Latex.

The paper should be a minimum of 5 pages in the above format. (Latex is strongly recommended).

Instructions and advice:

• Identify an area of specialization that you are interested in. Scan through all available sources to identify interesting, current research on a specific topic within the area. Students opting for the thesis option for their MSc, can use this literature survey to perform introductory-background work for their thesis.
• After you have done some initial investigation, consult with a faculty member, who specializes in the area you are interested in. (If you do not know who to consult for a specific specialization/topic, ask me and I will advise you on who to contact).
• With the help of the faculty member, you will clearly identify the topic and title of your review, as well as 3-4 relevant, recent journal or conference papers from where start.
• Once you finalize the topic and theme of your review, submit them to me by March, 20 (as stated above), in order to have the review proposal approved.
• Your review must include at least 8-10 papers published in refereed journals andconferences.
• Your review should be organized thematically (see slides on Writing Research Papers, section on Literature Review). You do not report on each paper you read one by one. Your review is not a summary of studies, but a synthesis of information which requires comparing

themes, methods and conclusions among the different works. A good way of keeping track of all this work and organizing your review is with the use of a synthesis matrix (literature review matrix).

An example of such a matrix is shown below. This matrix is taken from the survey paper:

“Toward the Next Generation of Recommender Systems: A survey of the state-of-the-art and possible extensions”, by G. Adomavicius in IEEE Transactions on Knowledge and Data Engineering, 17 (6), 2005. (I uploaded it on moodle).

This survey is on Recommender Systems, which is a topic of Machine Learning (and Data Mining) which falls under the general area of Artificial Intelligence. The matrix shows how Recommender Systems can be categorized as: 1) content-based, collaborative or hybrid, based on the recommender approach used (rows) and 2) heuristic-based or model-based, based on the types of recommendation techniques used for the rating estimation (columns).

On moodle you will also find another two survey papers. I strongly recommend that you go through these survey papers to get an idea of the structure of literature surveys and how they are organized (they also include literature matrices).

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