educational-design | Thought splinters

Educational Tools and Learning Theory

Sun, 12 Jan 2020 00:00:00 +0000

This article suggests a procedure to categorize the enormous amount of educational tools in the market. My approach uses a framework of three well-grounded learning theories: Instruction, Cognition, and Construction based on presentation modes (one-way teaching), dialog settings (two-way education), and collaborative scenarios (learning by mastering complex situations). As a proof of concept, I will use the Top Tools for Learning (TT4L), compiled by Jane Hart as a result of the 13th Annual Learning Tools Survey (published 18 September 2019).

Inspired by the yearly ranking of Top Tools for Learning (TT4L) compiled by Jane Hart, I wanted to know if there are global changes or trends in e-learning over the years. The ranked list of tools goes back 13 years and is available for all past years via the Wayback Machine. As a plan for analysis and for presenting results, I imagine a similar procedure as I have used in comparing static website generators over the years.

Inconsistencies in Jane’s categories

To compare the different rankings of the educational tools, I will need a classification of all apps/web services based on pedagogical reasons. For my purposes, the categorizations used by Jane Hart (see right column) is not useful:

It is the functionality of the tool itself, which motivates her division in different classes primarily. Therefore all categories contain the word ‘tool’ in their name. She distinguishes four main types:
1. Instructional tools
2. Content development tools
3. Social tools
4. Personal (and professional) tools
Her sub-categorization under the four main headings is not consistent and has changed over the years. Sometimes she uses ‘Content’ as a fifth type, or she includes ‘professional’ under the heading of ‘personal tool.’
Some tools are platform-dependent (e.g., Sharepoint, Keynote).
Even proprietary name for hardware is ranked in her list (e.g., iPhone, iPad, SMART board instead of smartphone, tablet, whiteboard).

Another much better division can be drawn from her beautiful infographics (see below) on top tools for higher education 2019. Here Hart distinguishes by pedagogical reasons between

Didactics (Formal Learning)
Discovery (Informal Learning)
Discourse (Social Learning) and
Doing (Studying).

Aside from the fact that ‘formal’ and ‘informal learning’ has another meaning (see the definition by the Council of Europe on formal, non-formal, and informal learning). I do not understand why ‘studying’ is different from structured learning in an academic setting. And why should ‘studying’ be linked just with the doing-bubble?

Usage of top tools for higher edudation in 2019. Infographics by C4LPT (2019): https://www.toptools4learning.com/edu100/.

Developing educational motivated categories

Three different learning/teaching modes. Infographics by Peter Baumgartner (2018)

Without going here into detail, I will use my categorization of learning theories published among others in The Zen Art of Teaching (PDF, 545 kB). This article outlines three different kinds of teaching modes:

Instruction or Learning I: Using static learning materials for reading, listening, and watching.
Cognition or Learning II: Using dynamic and elaborated didactical educational interactions for guided practice.
Construction or Learning III: Using collaborative scenarios for mastering unstructured situations.

This categorization results in different educational aspects for each of the three teaching/learning modes:

Kind of knowledge acquisition: static –dynamic/procedural – social.
Teaching modes: transfer – dialog/discussion – collaboration.
Learning strategies: study – practice – mastering.
Assessment methods: correct answers – applying adequate techniques, processes, and procedures – success criteria.
Teaching strategies: explaining – observing and helping – collaborating.

Even though my suggestion is coarse-grained as it distinguishes only three types, it has to overcome several problems of operationalizations:

One tool – several functionalities

Sometimes it is not easy to classify tools unambiguously into one of the three categories. Modern tools incorporate many features that may address different types of learning modes. Take, for example, the use of a learning management system (LMS). You can use it for various activities (reading PDFs, work through quizzes, or writing collaboratively into a wiki). In those inconclusive situations, I will utilize the highest predominant functionality of the tools. ‘Highest’ means that Learning II is more elevated than Learning I. This is not a quality rating of the different learning modes as all three modes have their value under certain conditions. ‘Higher’ or ‘lower’ address here the complexity of student activities.

But what is the ‘predominant functionality’ of a tool? I assume that every educational technology, every technology-supported learning environment, or internet application, implement a theoretical learning model – irrespective of opinions and beliefs of developers and teachers. In the case of LMS dominates guided learning interactivity (Learning II). Although teachers may use tools ingeniously for purposes not foreseen from the developers, I think that these inventing teaching strategies are not the mainstream.

Following other users on the web with the same subject interests, getting in contact with them, commenting their blogs, etc. entitles for Learning II but does not qualify for Learning III. I will rate only rate an educational setting as Learning III if learners produce (individually or collaboratively) an artifact for learning purposes.

General tools not intended primarily for education

In Jane Hart’s list of top tools are many products and web services not primarily developed for education, like word processors, email clients, file-sharing platforms, note-taking applications, browsers, search engines, etc. The reason for this broad mixture is the content of the questionnaire and their target group. Not learners or teachers, but mainly IT professionals and learning designers, addresses the survey. This orientation leads to two biases, as mentioned by Jane Hart:

only 22% of voters came from colleges and universities, hence the Top 200 list is therefore skewed towards workplace learning.
since only 6% of respondents came from schools, the EDU100 list this year focuses on tools for Higher Education.

Snippet of survey form for compiling the top tool list for 2020

Another issue favoring general tools are the questions themselves. See, for instance, the voting form for 2020 on the right ahnd side of this article:

A valid entry needs to include 10 different DIGITAL tools (resources, services, etc) – listed in any order (emphasis not mine).

So the questions ask for digital tools, not for digital educational tools. The consequence is that the top list with a few exceptions does not include subject-oriented software (e.g., apps for learning math, science, languages). This bias is an essential drawback as many e-learning scenarios use these kinds of content-related software.

The other side of the same coin is that there is a bias towards personal knowledge management applications. Software like email clients, browsers, search engines, etc., are unquestionably also essential parts of a learning ecology. Using these tools always goes together with (informal) learning experiences. But an educational taxonomy of tools for education should focus on apps or web services developed for e-learning. To distinguish those personal productivity instruments from tools explicitly dedicated to learning, I have designated them with Learning 0. Under this category, I also have classified support tools like converters or ‘neutral’ web resources like portals for icons, images, or other material.

Summarizing this subsection is the following types of tools designated with Learning 0:

General productivity: Word processors, email clients, browsers, search engines, converters, translators, grammar checkers, image/icon/theme libraries, and other supporting tools.
Personal knowledge management: Note-taking software, calendars, project management, skill validation.
Hardware: Smartphones, tablets, interactive whiteboards.

Developing learning material or using it?

Another complication arises from the fact that the educational judgment of developer tools can be seen two-fold:

from the authoring perspective, e.g., the resource development view
from the angle of using the developed product

There are content development tools which can be used by learner and teacher alike. Those apps or web services with various perspectives (e.g., blog services, screen-casting, curation tools) I will classify from their ownership. If it is easily possible that students get their personal instance, then I will designate it as Learning III.

With ‘easy’ I mean

that it is free available at least in a freemium variant for personal usage. This definition of ‘free available’ does not apply if learners are entitled to free usage only via an institutional account.
that it is easy to install either through the download of packaged software without any special additional technical requirements, such as using the terminal or running a server, changing functionality programmatically, or only through a simple online registration).
that it can be used easily in educational settings (no specialized knowledge necessary, learn time only 1-2hrs, no specialized hardware, platform-independent).

Note that every case has to be judged individually. For a correct classification with the mentioned rules, one has to look into pricing models, installation procedures, user interface, and typical application patterns.

Conclusion

To summarize my suggestion for an educational categorization of digital tools, I will define four types of apps, respectively web services:

Learning 0: Tools supporting personal knowledge management, productivity, and portals for web resources.
Learning I: Transfer of knowledge. The focus of learner engagement is primarily oriented to content to acquire factual knowledge.
Learning II: Applying knowledge, practicing procedures. The emphasis here is learner interactivity with dynamic content and dialog with teacher and co-learner.
Learning III: Collaboratively solving challenges. Essential is producing a useful output, doing something creatively and cooperatively.
Learning IV: Multi-purpose tool with features for Learning III and either Learning 0 or Learning I.

… to be continued

Update 2021-05-20: Originally, I have planned to reorganize Jane Hart’s data according to my set of categories. But unfortunately, because of other priorities, I didn’t have time so far. But the follow-up of this post is still on my schedule.

Data Science Education with coursera

Tue, 18 Jun 2019 00:00:00 +0000

Beginning in December 2016, I initiated a new personal enterprise: Learning the statistical programming language R to acquire competencies of a data scientist. With “learning enterprise,” I do not mean to get just interested in a new subject, to read from time to time a relevant book and to look into some web-based tutorials. No, with “enterprise” I mean a much more significant undertaking, namely to focus and concentrate several months on a systematic study for a new set of qualifications.

I started with a nine-part (June 2019: now ten-part) course specialization to data science, taught by three professors of John Hopkins University (Jeff Leek, Roger D. Peng, and Brian Caffo) and offered via the MOOC¹-platform Coursera.

One-Sided Course Philosophy

But after I have finished successfully two courses with certificates, I turned impatient with the teaching style and course philosophy. My dissatisfaction did not so much arise by these three professors but had more abstract reasons: The course has as target audience people who need certified knowledge to get into (a better) job position. People like me (experienced adult learner, who are already permanently employed and have no intention to change the job) who want to learn specific skills for their working needs, are not well supported. I do not need to provide evidence of some acquired general qualifications as most of the course participants do. I was instead looking for some Personal Knowledge [@polanyi_personal_1974-2], which I could combine with my life and working experiences. After a long time again in the learner role, I found it very strange that I had to spend much learning time on material arranged by other people which I didn’t need and had, on the other hand, to learn some stuff superficially or to skip altogether, which is essential for my use cases.

To exemplify the problem in more detail: In the third course on Getting and Cleaning Data we had to learn different ways to get data into the R environment: To download it with URLs, to scrap it from web pages, to load it from Excel sheets, to collect it via an API. But – for instance – we didn’t learn how to get data from an SQL database. I appreciate that all these different methods are conceivably relevant, and as a becoming data scientist, one should know how to apply them all. I also understand that it is impossible to learn all the different use cases in every detail. But I disagree that the best teaching method is an exemplary journey through some of these potentially essential methods!

A teaching strategy which would have better suited me and other self-determinded learners would have been a systematic and complete list of all approaches with their pointers to the relevant R packages and supplemented with some prototypical program snippets to get started. After a systematic overview and instructional material of all techniques, one should have the opportunity to choose those two or three methods which cover those skills which are essential for the particular learner. In practical assignments, self-determined learners learner should have the chance to be trained and to practice precisely only those procedures they are interested in.

Another example that coursera is not interested in the self-determined learner is their change in the billing strategy: Coursera made a switch to a subscription model for the Data Science Specialization. Instead, to bill per course, you have now to pay a monthly fee, which is counteracting self-learning and autodidacticism. To economize and save money for the course, one has to study fast and without looking for additional material, exercises, or implementations for his/her use cases. Consequences are a rigid study behavior where students follow the course design uncritically and without generating and posing questions. From an educational point of view, such a course of studies contradicts the psychological research on learning thoroughly [@ambrose_how_2010; @brown_make_2014].

How to support the self-determined learner?

Consequently, I stopped my course attendance and started to look for tutorials, courses, and other learning material that are more to the point for my needs. I have to confess that I didn’t know how exciting but also how difficult this self-paced and self-determined learning approach was, respectively is. Between us educators, we are talking routinely about self-determined learning [@deci_handbook_2008; @deci_overview_2012-1; @deci_self-determination_2008-2], but often it is just a plea without detailed tips on how to achieve it.

When we are preparing material for learners, we are restricted by the learning time of our students (measured in credit points) and have therefore to select from the available resources. This strategy poses a problem for the self-determined learner as we as teacher decide which material is essential to learn and which not. Even if we are experienced teacher on the subject material in question and know therefore which skills are generally more in demand in real-life applications, we will always be wrong whenever some of our students have different needs or requirements.

For the design of online courses, there is an easy solution. Even if every curriculum has a reference point for the average amount of learning hours a student should need to cover a specific subject, there is no restriction on quantity for uploaded material. When we quantify the necessary learning hours of each different subject branch we are going to provide, then we can leave the choices to the students. The only requirement they would have to satisfy is a certain amount of learning hours. Students have to choose from a list of options in such a way that they fulfill this requirement by the curriculum.

To provide a course design where all students have to learn the same material generates an anachronistic situation: We are living in times of opulence where for almost all products, many variants for individual choices are available. But not so in the educational sector where with cohort learning still the industrial mass production model is prevalent. It is time to change this now — and with online courses, we have already the appropriate learning environment for individualization ready at hand.

Résumé

Online courses should not only address learner looking for (non-)formal education and validated certificates but should also support the self-determined learner with its particular learning goals. Yes, my recommended educational strategy in the case mentioned above is a laboriously and hard-working solution in two aspects:

You need to work out all different learning paths with their answers, examples, exercises, and tests.
You need to provide an introductory section with a basic explanation of each branch so that students can make an informed decision.

I agree that these two requirements for the self-determined learner are additional work. But for MOOCs with their vast numbers of learners from different backgrounds, an individualized educational design would be valuable and economically feasible. Another advantage of the suggested instructional method to teach all approaches for a specific subject (e.g., ‘How to get data into the R environment?’) is that every one of these different procedures to choose from can be explained in exhaustive detail. As the various branches of the learning material separated in different self-contained modules, it is easy to add new methods or correct out-dated ones. With the proper design approach, each of these modules is an autonomous learning object that can be used in other (learning) contexts as well.

References

Massive Open Online Course ↩︎