Prompt

Directions for Curriculum Rationale

Each group will submit a curriculum rationale that includes the following components:

• Information about the Site

Give a brief description of the site for which you are constructing your curriculum.  What do you know about the context that will influence what you produce? What do you know (if anything) about the person/people who will be implementing your curriculum?

• Ideology/Theory

What ideologies or curriculum theories undergird your curriculum?  How do these ideologies/theories influence the design of the curriculum? Be sure to cite particular theorists as appropriate.

• The Learners

Who are they?  What do you know about them?  What assumptions are you making about how they learn and what is important for them to know?

• Overall Rationale

Identify the overall why, what, and how of your curriculum and explain why you made these choices.  This section may include a rough outline of topics to be covered and possible scope of the unit. It should be clear how the rationale fits the setting and is appropriate for the learners that you’ve described.

Please bring hard or electronic copies of your rationale to class next week for peer review.  The instructors will also give you feedback about the rationale, so please send us an electronic copy as well.

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Response

Curriculum Rationale
Celine Zhang, Lisa Jiang, Lucas Longo, Mohamad Haj Hasan

Information about the Site

The site we are working with is the Operations, Information & Technology (OIT) Department at the Stanford Graduate School of Business (GSB). The site is looking to add some online elements to their Base course in Data and Decisions. This course is an introductory course to probability, statistics and regression. The course is a mixture between theory, concepts and practical application in a business context. The course has been taught in the same way for the past 15-20 years, in a completely lecture-based format of mostly theory with limited hands-on application during class time. The culmination of the class is a practical project that is intended to model a real-world application of the concepts learned in class, and the students have an opportunity to work with real clients who have real data and decision needs.

The site would like to design and put all the theoretical and conceptual components of the course online, and utilize class time for more engaging practical applications, clarification of the online content and general discussion. The theoretical part of the course is almost perfectly suited for online consumption for the following reasons:

1. Students with different backgrounds in the subject can learn at their own pace, repeating concepts and formulas as many times as they want.
2. The content to be put online is very much “passive” in the sense that little is lost from a one-sided online lecture.

The site would also like to have some adaptive assessment solutions online that would act both as a feedback mechanism for students as well as an observational tool for the teachers as to how students are learning and progressing in the class.

The person leading this initiative is Allison O’Hair. Allison has experience in designing and implementing online content for MIT Sloan and is very knowledgable on the medium of online education. It is interesting to note that although the OIT Department is leading this initiative, the course is technically under the Economics Department at the GSB.

Ideology/Theory

Our curriculum would be undergirded primarily by the Dewey’s concept of progressivism in that we hope to design learning experiences that drive students’ innate desire to learn. According to Dewey, the educator’s role is to set up the right conditions for transfer, rather than teach lessons in isolation. The sign of a mature learner is then someone capable of both identifying and solving their own problems. This focus on “transfer” to enable students to apply their learnings beyond the classroom would definitely be a key learning goal in our curriculum, with ample class time devoted to discussing practical applications of concepts and a real-world project for assessment.

In addition, Dewey placed great emphasis on the interaction between internal and objective conditions for curriculum design. He contended that curriculum construction was always contextual, and that “the trouble with traditional education was not that it emphasized the external conditions that enter into the control of the experiences but that it paid so little attention to the internal factors which also decide what kind of experience is had”. Personalizing the learning experiences for students based on their “internal” conditions, such as their backgrounds, prior knowledge, social-emotional skills and so forth, would be key to effective learning. Ideally, these learning experiences should help prepare students for later experiences and drive continued learning. Selecting and creating learning experiences – be it direct instruction, class discussions or assessments – that are tailored to students’ backgrounds will very much be a core consideration in our curriculum design. Our ultimate goal would be to equip our students with both the desire and skill to continue enhancing their understandings of probability, statistics and regression application in a business context.

In a similar vein to Dewey’s progressivism, Bruner’s theory of emphasizing structure, transfer, and students’ readiness for learning would also underpin our curriculum design. We agree with Bruner that the ultimate goal of education is to help students “learn how to learn” and facilitate transfer. The emphasis on structure, then, is critical because a deep understanding of structure is also a deep understanding of how things are related, and therefore permitting transfer. The implication for us would be to delineate the key concepts to be covered in the course and sequencing them in such a way that “earlier learning renders later learning easier … by providing a general picture in terms of which the relations between things encountered earlier and later are made as clear as possible”. In terms of readiness to learn, we are cognizant that the learners in our course may come equipped with different levels of understanding and aptitudes, and it is our hope to create a curriculum that provides satisfying learning experiences for students regardless of their existing preparedness for the course. We want our curriculum to result in a class that stimulates our students’ desires to learn.

The Learners

The learners are first year MBA students at the GSB (MBA1s). The Base level of Data and Decisions targets students with little or no background in the subject, however the class may have some students with decent background who have chosen to take the Base level instead of the Intermediate or the Advanced levels of the subject. The site is targeting a launch date of Winter 2017 to pilot the course, and it would be presented as an opt-in option for eligible Base students.

The learners come from a wide range of background knowledge and experiences. While most learners were exposed to some part of the content in high school or college, many of them may not have been exposed to or have applied the concepts at work. We would also assume that the learners have different computer skills. This is important because the course uses some Excel and quite a bit of R, a programming language and software environment for statistical computing and graphics. R is especially important for the final project where learners work with a real company and real data to help the company answer some critical business questions using the data. In addition, the learners are in the process of getting a wider business degree, and we assume they are more interested in the business applications of the content as opposed to the details of the formulas and their derivation. For example, it may be more beneficial to know the idea behind variance, how it’s calculated in Excel or R and its use as opposed to knowing the exact formula.

It is fair to assume that the MBA1s are also busy with many social and academic events, which means that there attention and dedication to the course will be spread thin. MBA students, in particularly, care deeply about ‘authentic’ learning experiences and will only devote their time and energy to topics that they perceive as having direct connections to their professional pursuits. We also assume that all MBA students have the appropriate technology affordances for online learning – access to high quality internet access and up- to-date computers to stream videos and run statistical programs.

Overall Rationale

The Data and Decisions course was originally designed as a support course, or prerequisite, for other courses such as Finance and Accounting. It was intended to provide a basic overview of how to use data to extract information that supports decision-making. Since then, specific topics have been added or subtracted from the curriculum to be more focused on data analysis than the calculation of probability and statistical procedures. Students will not only learn methods of using data but, more importantly, should be able to build models and critique them. The hope is that students will become intelligent consumers of data who can look at it and interpret it.

This shift towards decision-making based on data analysis is now central to the current redesign of the curriculum. The goal is to shift from the ‘teaching of formulas’ to doing problem sets, discussions, and application of core concepts. Given this shift in focus, we believe that the teaching of formulas and procedures tend to be more linear and repetitive and thus great candidates for being presented as online content, instead of using valuable classroom time.

Online content also corrects for student’s previous knowledge and pace. Problem sets can be personalized for each student’s level of understanding, thus ensuring everyone’s preparedness for the course’s learning progression. Discussion forums and peer-review mechanisms can also provide different learning opportunities for those who have different learning styles and prefer more collaboration or explanations in different ways. It can also serve as a great formative assessment for teachers to identify common misconceptions and course correct. The implementation of these features and what technological platform will be used remains undecided.

The original course content sequence is as follows:

1. The first area, probability, provides a foundation for modeling uncertainties, such as the uncertainties faced by financial investors or insurers. We will study the mechanics of probability (manipulating some probabilities to get others) and the use of probability to make judgments about uncertain events.
2. The second area, statistics, provides techniques for interpreting data, such as the data a marketing department might have on consumer purchases. Statistical methods permit managers to use small amounts of information (such as the number of people switching from Verizon to AT&T in an iPhone test marketing program) to answer larger questions (what would AT&T’s new market share be if the iPhone is launched nationally?)
3. The third area, regression analysis, is the set of techniques that allow companies to build statistical models of different facets of their businesses. Examples include predicting which movies a customer may like based on her past movie ratings (e.g. Netflix), predicting the sales price of a house (e.g. Zillow), or predicting the sales response to a new ad (e.g. Google).

Original course grading

• Class Participation Evaluation 10%
• Mid-term Exam 20%
• Homeworks 15%
• Regression Project 20%
• Final Exam 35%

The proposed course content sequence attempts to flip the sequence so that students have an end goal in mind and learn in a ‘need-to-know’ basis.

1. Final project – Phase 1
   1. Show previous final projects as examples
   2. Explain what quality work looks like
   3. Show final project grading rubric
   4. Select a real company to obtain data from
2. Regression analysis – Phase 1
   1. What is it
   2. Examples of how to use it
   3. Underlying concepts  
      1. Regression
      2. Statistics
      3. Probability
3. Final project – Phase 2
   1. Data manipulation and clean up
   2. Desired data representations or key performance indexes
4. Regression analysis – Phase 2
   1. How to do it with your own data
   2. Underlying concepts  
      1. Regression
      2. Statistics
      3. Probability
5. Final project – Phase 3
   1. Data analysis
   2. Present project and results
   3. Peer-review sessions
6. Conclusion
   1. Cases and further discussions
   2. Feedback from professor and company