17 answers

How will the field of education be affected by data science?

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I am asking this because I am interested in becoming an instructional designer. #education #data-science #analytics

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17 answers

Kim’s Answer

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Data science as we define it 'captures structure or unstructured data' often through a scientific method; whether it is qualitative (opinions or perspectives) or quantitative (numerical values that reflect points in or over time). Currently, education is affected or influenced by data science in a few major ways. In a brief, here are a few examples.


Technology- A LMS system can track, monitor and report large amounts of user data for instructional analysis and feedback.


Marketing- Quantitative or qualitative data is used as evidence to persuade people either for the purpose of education sales or recruitment for participation of education initiatives that affect student academic outcomes.


Trend Insights- Different types of data are often used to determine strategic planning for small or large projects that help with hiring new educational staff, faculty or reform efforts in the area of diversity and inclusion.


Adaptive Technology- Data science has been a huge help to companies like DreamBox, that rely on data to assist with modifying lesson plans (adaptive technology) based on a student's learning progress. For example, as a student moves through an adaptive software lesson plan, the lesson plan will not advance to new problems unless the student demonstrates competency in the initial problems presented to them.


Hope this helps!

Kim recommends the following next steps:

  • Take a free online course by Edx.org in the area of data science in order to learn more it. This will help you infer new ways of thinking about how education and data science continue to blend with technological advances.
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Craig’s Answer

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Increased data science in the field of education will produce greater evidence of effective learning practices and technologies for implementation in the field. In my experience, greater data collected through research and evaluation has proven to improve the quality of instruction and learner outcomes.

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Tara’s Answer

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There is an entire emerging field of learning analytics dedicated to analyzing data scraped from learning management systems and figuring out what works best for different subjects and types of learners. In the (not too distant) future, there could be an assessment about how you learn best that you take at the beginning of a course, and then your content, activities, and assessments would be curated differently based upon your learning preferences.


Another way education will be affected by data science is the influx of linked data about jobs. As education and the workforce continue to blur lines and converge in the future, it will be important that learning outcomes are connected to the changing needs of the workplace. The current standards being used (O*Net) to align learning outcomes to workplace competencies are only being updated about every 6 years. As we know, in the future of work, technologies will be changing at an exponential rate, impacting the competencies needed. There is a need for realtime data that reports back to educational institutions what skills need to be taught.

Tara recommends the following next steps:

  • There are some emerging interdisciplinary programs that focus on the cross section of data and education like GWU's human technology collaboration https://gsehd.gwu.edu/programs/phd-human-technology-collaboration-cross-disciplinary-team
  • Here is a report produced by the US Chamber of Commerce Foundation on some relevant projects https://www.uschamberfoundation.org/sites/default/files/T3Phase1_Report_FINAL_0.pdf
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Kathy’s Answer

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As you can probably guess, LMS software is becoming more and more capable of tailoring a virtual learning experience to the learner based on their performance. Not only can the software run reports for us to know where all our learners are in a program/how they performed, but we can also program these systems to provide extra courses, tailored additional feedback, or customized learning experiences based on the input they give during a program.


Another thing that's changing is the expectation of measuring learning results. It used to almost be above and beyond if we could display exactly what behaviors changed as a result of learning. Now, with analytics as powerful as it is, it's a given that you will be expected to collect data that demonstrates that your training actually produced an outcome (worked).


Everyone else has to measure job performance and provide clear performance data for their employees, too. That means when you work for a client or a company to design training, they will often have data that empowers you to pinpoint where the weak or blind spots are for your audience, and design specifically to those learning objectives. It puts a lot of power in your hands to offer targeted training. For instance, being asked to "fix the sales team" is much more vague than if they say "here are our key performance metrics from the last 12 months, and where we saw the gaps. Can you teach to these gaps?"


Data is generally our best friend in this industry. I think the rest of the workplace is finally catching up in a way that lets us do our job in a spiffier, more awesome way.


I'd strongly suggest you investigate some of the free content on https://www.elearningguild.com/. They are the industry leaders on researching how tech and learning intersect. DEVLEARN, their annual conference in Las Vegas, would be an outstanding opportunity if you have an employer who will send you.



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Dagmar’s Answer

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The fundamental question that I ask myself every time before designing any training class or program is, how will I know the training is successful? There needs to be a clear measurement. Whether you use Kirkpatrick method, Quality Matters or other methods to evaluate training, it is important to have data. The basic level is qualitative and can be as simple as a questionnaire of what students thought of the class, instructor, and if met their expectations. The more in depth levels of evaluation focus on how students applied the knowledge and skills taught in class. Certifications are very effective to measure not only students ability to retain but also to apply on their job. For kids-and young people apprentice can be measured by completing tasks. The future of data mining and artificial intelligence will continue evolving and showing itself in learning. Data science is so important in linking learning to impact and creating successful students.
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Patricia’s Answer

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If you are interested in entering the field of education from a data science point of view the best place to start is to research and track the evolution of the use of xAPI tracking. In simple terms, this is the way that a person's learning habits are tracked in an LMS and it can also be used in developing 'optimized' learning paths for a learner, sometimes known as personalized learning.


The key to understand is that xAPI allows educators to track what is called 'informal learning', that is, what is accessed outside of a formal course/quiz sequence. Prior to the development of xAPI the SCORM standard only could track those formal courses. Now we can track any 'optional resources' such as a video etc, that is accessed outside the course. What this means is that we can, in the long run, track how learners with a high performance rate learn. This will then influence how we teach new learners.


So you can see, data is used not just to 'gate' a learners path such that they only access the next level of learning when they 'pass' the current level. That has been happening for awhile.


The next best thing that big data will offer us is to learn from the learner 'best' education or training practices.


One other use of AI/ML and big data is to create 'automated' learning that combines both of the former. It learned from all learners and combines that modeling with learning from the current learner and personalizing the learning from there. There is a cool diagram and explanation of an example here: https://www.dev4x.com/ourapproach#moonshot-education-project-detail


Anyway - this is what I am tracking. Feel free to reach out to ask more.

Patricia recommends the following next steps:

  • learn Javascript and JSON which is the code that xAPI is built from. https://learnxapi.com/
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Diane’s Answer

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Hi, there!

Data science is an integral part of Instructional Design and e-Learning. When students and learners complete courses, exams, and knowledge checks, it's vital to be able to extract data about each participants scores, time spent on learning materials, etc. LMS' are designed to extract this and other data to give instructional designers insight into their work. Measuring outcomes is generally a requirement, so data science plays heavily into our field.

Diane recommends the following next steps:

  • Perhaps it would be an important next step to research the types of reports (data) popular LMS can extract, and how it's used by designers.
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JC’s Answer

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Data science will be one of several new competencies across most sciences. I recently wrapped up a specialization in data science on Coursera and the topical coverage was vast, from machine learning to statistical inference to R programming. You see this happening today throughout the K-12 curricula, and pointedly so in most lower division college courses. Many college breadth requirements now include a data science course or at least a course that exposes students to ds.


I agree with others about how our research methods and evaluations can be enhanced with data science. But let's not forget that a strong understanding of data science presents opportunities where they were once to cumbersome to collect and tabulate.

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Chuchun’s Answer

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One of the age-old questions about education is how to personalize information so that an individual knows how to use the knowledge as soon as they get it. For this reason, collecting data is key to better knowledge transfer and application in education. As data accumulates, educators can create better learning environments, content, and delivery methods that can make learning relevant, timely, and most importantly, fun!
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Raymond’s Answer

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I feel that data science will only enhance this field. I am now using VR and AR in my design, and this captures the attention of the students. Every year technology increases and gives an ID more to work with. I don’t see how Data Science will replace this.

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Doreen’s Answer

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The field of education will be extremely affected by Data Science and its availability to be obtained via the internet. We have so much data that can be shown by creating visuals to represent the enormous amount of data produced or found.

The job of an instructional designer is to create a unique way for all this data to be broken down into chuckable learning nuggets in an appealing and easy way to learn. Using our knowledge of "how people learn", best practices of online education and various technology tools such as images, video, audio podcasts, short written text, animations and gamification (game-based learning) we are able to design highly effective learning platforms for the world.

Doreen recommends the following next steps:

  • You can Google Instructional Design Facebook groups or Linked-in groups that allow you to follow current trends, talk to people in the field or just getting in the field. Many Instructional Designers are former teachers like myself, but they also come from vast background. The people in my facebook groups are amazing and someone is always around to help you get an answer to problems or questions you may have.
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Radhia’s Answer

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In technology and marketing as well as production

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KathleenMichael’s Answer

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There is a lot of good information on the internet that can help you answer this question. Take a look at the following site

https://towardsdatascience.com/5-ways-data-science-is-improving-higher-education-b5bf402d50c4

Data impacts everything. The more data we have available the better our decision become. For example, if you want to be an Instructional Designer and you believe you have a very good idea on how to help students learn, which could be implemented in online or traditional classroom teaching, you would first want to design an experiment to test your hypothesis. You might find out that your idea truly does work and the results are statistically significant. However, you might also find out the opposite. Either way, you and the rest of the scientific community have gained valuable information.

Your experiment provided data that either supported or refuted your hypothesis. This is how data helps. Remember, in science nothing is ever “proved” or “disproved”. Our hypotheses are either “supported” by our findings (data) or it is not supported. Data does not mean only numbers!! Numerical data is quantitative - it can be measured and is represented by numbers. Then there is qualitative data. This is data that is derived from study’s that are designed to be answered using language. For example:a phone survey in which the interviewer asks you questions and you respond with a word, or a statement (sometimes you say even more).

This qualitative data must be examined and similar concepts or words are then assigned a numeric value so an analysis can be conducted. Consider the following: you are taking a survey and one of the questions you are asked is, what is your gender? You respond “female”. Well the word female can not be statistically analyzed. The person who designed the experiment (survey) already knew this would be a problem. So, they decided that the word “female” would be given the number 1, male would be given the number 2, and if someone was to say they did not want to provide the information, that would have the number 3.

Now, by assigning a numerical value a statistical analysis can be completed. This is all part of working with and understanding “data”

Good luck with your question.

Dr. Perrine
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Jacob’s Answer

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Data science is incredibly important as a measuring and strategic tool. But a small word of caution to go with the rest of this excellent advice: data still requires interpretation. Any given result is not "good" or "bad" by definition. You still have to do the hard work of determining the relevance of your data, even before considering its actionability. You have to look for complications, problems, possible errors, misuses, etc.

For example, a colleague of mine was recently asserting that our data told us that company associates weren't completing their electronic training. But I decided to dig in to that data and found a bug that was preventing our LMS from recognizing when an associate had completed the course. So we actually had a much higher percentage of employee participation than the data showed. Our data was wrong, and so was any interpretation that had been made as a result of that flawed data. We had to "start over" in our analysis.

All data collection and use has a constant weak link: us. As educators, evaluators, and data scientists, we design the collection methods as well as the interpretation of said collection. We often have a vested stake in the results turning out one way or another. We are often unaware of our biases, of our continuous influences on the whole process. Things like Heisenberg's Uncertainty Principle (also known as the "Observer Effect"), filtering the Signal from the Noise, and a lack of skilled data analysts are among the wide variety of problems for data scientists to monitor and account for. Even if you know enough to understand and validly interpret the significance of a result, you still must be capable of communicating that result to the people who need to understand it (and most likely won't without a compelling explanation at a fifth grade level).

It's your job to be aware of these weaknesses at every step, to try to account for it in your measuring, collecting, and interpretation practices. The people who don't aren't just unproductive: they are actively counter-productive. If you aren't aware of this weakness, you will be incredibly confident of the wrong things; and that's the kind of situation that can destroy jobs, departments, even companies.

Jacob recommends the following next steps:

  • Practice your analysis and critical thinking skills. Healthy skepticism and curiosity will help you avoid a number of data-related pitfalls.
  • Practice your communication skills.
  • Get some psychology training; as a data scientist in the education field, you will be regularly thinking about people in an abstract, number-crunching way. You must do all you can to remember and account for the individuals behind those numbers.
Jacob, as an educator perusing questions and answers today, I found your feedback to be extremely thorough and helpful. Jennifer Tse
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Aaron’s Answer

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Data is everywhere in the field right now! I'll share a few examples that can hopefully help you see the scope of how important understanding data science is to education right now.

I'm an instructional designer at a software company, so a lot of the learning content I'm creating goes straight to our customers. There are so many different types of learning being requested of us that we tend to use data to decide what projects to prioritize. We look at views on our website, trending topics on sites like Linkedin and Google, as well as our company's sales numbers to make sure that when we spend time making that amazing learning content, there is a large audience out there that wants to see it.

Obviously, we don't always get it right. So, we also have data attached to every piece of content we create! This helps us see what types of content are preforming well and figure out how we can make more of those pieces in the future. This data also helps us go to our bosses and say "Hey we had "x" amount of views on our videos last month, so we need more money to buy better equipment and hire more people so we can make more videos!"

These are the two biggest influences on my day to day as an Instructional Designer, and I think they illustrate well how important data, and data science understanding, is for learning professionals.

Aaron recommends the following next steps:

  • Check out this book if you want to dive deeper. We should always be thinking about the story we are telling as learning designers, so understanding how data can tell stories is key! https://www.google.com/books/edition/Storytelling_with_Data/rRSRCgAAQBAJ?hl=en&gbpv=0
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Shermaine M.’s Answer

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There are 10 areas of learning and development (L&D). Data science is a critical component of L&D. We use data science to enhance the learning experience and then allow big data to assist in strategic leadership development. Learning analytics are used as well to evaluate interventions and programs to demonstrate the Return on Investment (ROI). The Association for Talent Development (ATD) is a valued resource among Instructional Designers desiring answers to such questions. Below are links to get started.

https://www.td.org/magazines/td-magazine/become-a-data-science-expert

https://www.td.org/virtual-training

Shermaine M. recommends the following next steps:

  • Browse www.td.org
  • Subscribe to TD Magazine
  • Connect with local ATD Chapters
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Ryan’s Answer

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Awesome question! And glad to see you're interested about the future. I can promise you these:

The training's created for organizations will be based on the "return on investment" and based on data pulled by surveys and other methods of gathering large information.
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