Tagged Assessment

Student Learning Objectives – Part 4

This post is part of a series on student learning objectives (SLO’s) for both curriculum and courses. The SLO’s in this post are course level, specifically topical objectives for an “Introduction to Data Science” (Data 151) class for new students. Love them or hate them, student learning objectives are a part of higher education (I for one appreciate how they provide focus for curriculum and courses).

The last post focused on high-level learning objectives for the course “Introduction to Data Science” (I’ve repeated them below for reference). Those are certainly the big picture, but those four objectives are hardly enough to really design day-to-day lessons around. Data 151 also has seven topical objectives tied directly to those general objectives and modeled after Paul Anderson’s DISC 101 course objectives. I’ll tie each topical objective back to the course’s overall goals.

General Course Objectives:

A. Students understand the fundamental concepts of data science and knowledge discovery
B. Students can apply and perform the basic algorithmic and computational tasks for data science
C. Students develop and improve analytical thinking for problem formulation and solution validation, especially using technology
D. Students prepare for success in a world overflowing with data.

Topical Objectives:

  1. gain an overview of the field of knowledge discovery (A)
  2. learn introductory and state-of-the-art data mining algorithms (A,B)
  3. be able to distinguish and translate between data, information, and knowledge (A, C)
  4. apply algorithms for inductive and deductive reasoning (B,C)
  5. apply information filtering and validation on real world datasets (B,C)
  6. understand the social, ethical, and legal issues of informatics and data science (A,D)
  7. apply data mining, statistical inference, and machine learning algorithms to a variety of datasets including text, image, biological, and health (B,D)

Four of the topical objectives (1,2, 3 & 6) focus on guiding students towards understanding the fundamental concepts behind data science. One can hardly call a course an “introduction” without giving an overall picture of the field (Obj. 1) or spending time understanding key tools that practitioners use (Obj. 2). While I fully anticipate that the state-of-the-art algorithms will change, the basics like k-Nearest Neighbor, k-Means, and Decision Trees will certainly not. These algorithms provide a nice gateway into understanding the ideas of learning from a collection of data (Obj. A).

We also know in data science that what you can learn from a data-set is limited by the quality of the input data (like a lot of other things in life, garbage-in = garbage-out). Objectives 5 & 7 articulate the sorts of data that will be used in the course, both real-world data and a mix of prepared/known data sets. These data sets provide a way to actually practice Objectives 2 & 4 in more than just an abstract way. I want students to walk away from this class knowing how practitioners actually make use of algorithms. Students need to get their hands dirty putting some of those algorithms to work (Obj. B/C).

Now, I think it’s important to note here that in their projects and general work, I’m not expecting a really deep understanding or application of the algorithms. That’s saved for two later courses, one explicitly on data mining and the other their capstone sequence. In Data 151 they should be learning enough to continue learning on their own, understand and interact with people who are really doing this work, and to grasp how the ideas can and are shaping the evolution of various disciplines or industries.

While Objectives 2, 4 & 5 articulate using data science skills, Objectives 2-5 have a second layer as well. These objectives aim to have students think about the implications and knowledge that comes from the data science process. This course is about more than just data engineering or data mining, it’s really about the questions and, well, science that can done with data. It is only when students can understand the processes of both inductive and deductive reasoning for science, or transform raw data into actionable knowledge that they become aware of the true power of the field (Obj. B/C).

Last, but certainly not least, Objective 6. As we know from Spider-Man (and some other great speeches), “With great power comes great responsibilities.” If you believe, like I do, that data science could dramatically change what we know and how industries and society is run… then I hope you are also a little nervous, perhaps occasionally terrified. Because if we DON’T talk about the social, ethical, and legal issues surrounding informatics and data science we might well end up with something like Ultron (the artificial intelligence gone bad in Marvel’s “Avengers: Age of Ultron”). More likely, we’ll end up with biased learning algorithms that perpetuate injustices or inequality. Making sure students have at least started to think about these sorts of issues may not prevent them from happening, but it is one (in my mind necessary) step towards that goal (Obj. D).

Together this is a pretty hefty set of things to accomplish in a semester. All in all though, I think they serve as a great lead into the entire field, and the overall goals of Valpo’s Data Science program (described in previous posts). Even if a student only takes Data 151 (as some certainly will), they will leave with a broad understanding of the field, enough knowledge to interact successfully with experts, and enough insight to see the real value that the effective and intelligent use of data can provide. I hope my business students are now prepared to be the “data-savvy business managers” that McKinsey & Co. described a few years ago and that the rest (C.S., Math and Stats) can work with, or become true data scientists, engineers, or creators.

Student Learning Objectives – Part 3

This post is part of a series on student learning objectives (SLO’s) both for curriculum and courses. The SLO’s in this post are course level, specifically for an “Introduction to Data Science” (Data 151) class for new students. Love them or hate them, student learning objectives are a part of higher education (I for one appreciate how they provide focus for curriculum and courses).

In many ways, the general course SLO’s for Data 151 mirror the SLO’s for the program as a whole. Students need to leave with an understanding of what data science is, know about the basic algorithms, and be made aware of the ethic and moral issues surrounding the use of data. Data 151 is intended to be a hook that draws in students from across our university to learn about data and then consider adding a major in Data Science. It also draws in juniors and seniors in less technical disciplines like business. This  may in turn make Data 151 the only course where a student explicitly thinks about data. The major difference between the curricular and course SLO’s is the depth of understanding I expect students to leave the course with (as opposed to the program). This is most clear in the first two SLO’s below.

  1. Students understand the fundamental concepts of data science and knowledge discovery
  2. Students can apply and perform the basic algorithmic and computational tasks for data science

As said, these are very close to the first two SLO’s for the whole curriculum and related to both their ability to communicate data science concepts and also their ability to implement solutions, though in both cases with lower levels of expertise. Data 151 has two additional SLO’s that target the broader (potential) audience for the course (in addition to continuing majors). These are:

3. Students develop and improve analytical thinking for problem formulation and solution validation, especially using technology
4. Students prepare for success in a world overflowing with data.

In many cases, students in Intro to Data Science are still gaining experience (aren’t we all?) with general problem solving skills. Perhaps (to my mind) one of the most under-taught skills in STEM courses is how to actually formulate and structure the process of solving a problem. In many, many cases, a significant amount of time can be saved in the execution of problem solving by carefully planning out how you are going to explore or solve a problem. Data science even has this explicitly built into several locations in a typical workflow, specifically performing exploratory data analysis and planning for solution validation.

Meanwhile, the final objective is meant to really be a catch-all. The field of data science is changing incredibly rapidly, as are the ways data is generated and used. I wanted Data 151 to be something that is capable of covering current, bleeding-edge topics. This SLO also nicely encompasses my plans to bring in alumni and current practitioners as speakers to give the students insight into what future jobs might look like. Bringing in these speakers also provides a chance for students to get an industry perspective on workflows and processes, something that can be very different from academia’s problem solving process.

These SLO’s are pretty high-level, but intentionally so. At Valpo, we’ve got both “course objectives” and also topical objectives. My next post will take a look at the specific, topical objectives for Data 151, which deal with the more nitty-gritty topics of what will actually get covered in Data 151.

Student Learning Objectives – Part 2

This post is part of a series on student learning objectives (SLO’s) both for curriculum and courses. The SLO’s in this post are curricular level and address soft-skills and versatile expertise. Love them or hate them, student learning objectives are a part of higher education (I for one appreciate how they provide focus for curriculum and courses).

Data science is acknowledged as an interdisciplinary field where a practitioner must have both broadly transferable skills ( data skills that apply to any data problem) and specific skills/knowledge within domains that questions arise in. So, a successful data science graduate must have domain knowledge, but it could be ANY domain knowledge. Moreover, they need to not only have domain knowledge but be able to actually transfer the data skills they’ve learned in the program to actually solving domain problems. And we need a student learning objective that captures that. Therefore, our third SLO is:

3. Students will be able to apply the skills and methods of the major [data science] for problem solving in data-intensive fields such as economics, meteorology, and biology, among others.

This objective lets us both capture the need for learning data skills to the point of transfer without specifying exactly what fields the student actually transfers their skills to. Since Valparaiso has its roots in a liberal arts tradition, this was particularly important since we want to encourage students in any discipline at all to consider adding a data science degree. While there are obviously more ‘traditional’ data-intensive fields (as listed), there are projects in any discipline that can make use of data skills, even as esoteric a field as studying historic french cookbooks (we have a computer science student helping with that project…). In practice, this objective can be met by students in a variety of ways, but most often by adding a minor or taking one of a specific set of data-centric discipline courses.

The final curriculum SLO I want to discuss deals with a (in one sense) completely non-technical skill, but stems from Valparaiso’s core values as grounded in the Lutheran tradition.

4. Students will be aware of and engaged with the use and misuse of analytical and statistical data-derived conclusions in the wider world

The news recently has covering many issues related to ethical use of data. Cathy O’Neil’s book “Weapons of Math Destruction” also highlights several cases of significantly unjust uses of data or data models. The Philosophical Transactions of the Royal Society even released a special themed issue in Dec. 2016 on ‘The ethical impact of data science‘. As a religiously based institution, Valpo cannot simply ignore training students in the potential pitfalls of building data models. But, even beyond that I believe that across the field, data scientists must think about, and understand, the moral, ethical and social impact of the projects they work on. I believe every member of our society has a personal responsibility to act in ways that support the common good, even if/while they are making decisions to promote their own good. Without proper training it is far too easy to overlook the possible bias inherent in collected data, designed models, or even the implementation of action in response to discoveries. The impact from a less than thoughtful choice or model could severely impact hundreds, thousands, or even millions of people. I certainly don’t want look back and realize I had trained that data scientist on my conscience…

Student Learning Objectives (Part 1)

This post is part of a series on student learning objectives (SLO’s) both for curriculum and courses. The SLO’s in this post are curricular level and address soft-skills and versatile expertise. Love them or hate them, student learning objectives are a part of higher education (I for one appreciate how they provide focus for curriculum and courses).

While there definitely is still debate about what exactly should be included in a full curriculum (Post forth-coming on the ASA’s guidelines), there’s definitely some consensus on the basic knowledge needed to succeed. That consensus centers around (1) needing a mix of hard-skills between Mathematics, Statistics and Computer Science (MS-CS) and (2) that data scientists (or really students in general) will need a variety of soft-skills. For example, respondents to O’Reilly’s 2016 Data Science Salary Survey reported that of the tasks they had a major involvement in 58% spent time “communicating findings to business decision makers”, 39% were “organizing and guiding team projects” and 28% had to “communicate with people outside [their] company”. This leads to the first SLO:

1. Students will demonstrate the ability to communicate effectively about mathematical and statistical concepts, as well as complex data analysis, in both written and verbal formats.

Phrase that SLO however you like, but I suspect you’ll find nearly all data science programs will need to have something along those lines. Our next SLO focuses even more on the hard-skills. We know our students will need technical skills including programming, software design, and experience working with big data. However, if you are reading this blog, you probably are aware enough about the high volatility and rapid changes that are occurring in the business and academic world of data science. Significant software workflows exist now that didn’t exist 6 months ago (heck, probably 2 months!) for answering data questions. Taking these into consideration, our second SLO addresses the skill need however phrased in a very generic way so that it will maintain its long-term viability.

2. Students will be able to implement solutions to mathematical and analytical questions in language(s) and tools appropriate for computer-based solutions, and do so with awareness of performance and design considerations.

With these two objectives, from our perspective we’ve covered all the generic skills that a student with any data science degree is going to need to be successful at. Part 2 will tackle SLO’s that relate to application knowledge and ethics.