The following is post I guest-authored for, and originally appeared on Getting Smart...
Noah is a 17-year-old multi-lingual
student. He can speak six languages even though his family speaks only English
and his public high school offers classes in only two non-English languages.
Noah didn’t have formal opportunities to pursue his linguistic interests, so he
took matters into his own hands, discovering online tools and social networks
for self-directed learning.
Noah’s story provides some good examples
of the kinds of data and technology enabling do-it-yourself learning.
Generation Do-It-Yourself (GenDIY) has
unprecedented opportunities to chart their own course for lifelong learning as
part of a career pathway, to reach a personal academic goal, or just to satisfy
a curiosity.
The data used to match learning
experiences with personal needs, preferences, and ability levels, and data
within online learning applications to provide continuous feedback, are
empowering learners like Noah to move beyond the constraints of traditional
education.
Do-it-yourself learning is taking place
on two levels:
- Formal systems of education are adopting student-centered options, giving students voice and choice, and visibility into how short-term choices support longer term career goals, and
- Learners of all ages are acting on their own, discovering and using technology enabled tools to reach their own learning goals.
Prior to high school Noah took an online
course in Latin. He worked through a book and viewed videos at his own pace. At
the time Noah was home schooled, but schools across the U.S. and around the
world are also leveraging a rich set of online options to offer courses that
they cannot staff. Course choice opens doors for students,
especially in communities that cannot attract teachers with specialized subject
matter expertise, or cannot fill a class with enough students to justify the
course.
After he discovered his interest in
language learning, a friend told Noah about a free language-learning tool that
he happened to read about in a technology blog. That tool was Duolingo,
the award winning free website and app.
Data for Discovery
Noah was fortunate to have a friend point
him toward Duolingo, but data is also helping the GenDIY self-discover the
right DIY learning tools and opportunities.
Linked data on the Web supports discovery
of learning resources (courses, apps, learning experiences, and social learning
opportunities). Metadata (data about data) is being used by the
major search engines to better filter search results to meet learner needs and
preferences. Publishers of learning resources tag web pages with metadata
attributes, such as specific competencies addressed and intended audience, in a
format that the search engines can read. Metadata may include tags about
accessibility of the resource, such as if a video is closed captioned for the
hearing impaired. This helps the self-directed learner find resources to fit
personal needs and preferences. Schema.org is a standard for tagging web content
developed through collaboration of the major search engines such as Google,
Yahoo, Bing, and Yandex.
“Paradata” gives DIY learners indicators of
learning resource usefulness, for example, many Facebook “likes” for a language
learners group increases the visibility of the group and becomes a paradata
assertion about its usefulness. Likewise social media posts with links to a
page describing a learning resource say something about its popularity, or a
formal endorsement of the resource by an organization (such as a state
education agency) may be captured in a public repository, such as the Learning Registry.
Gamification and Intelligent
Tutoring Data
With the help of Duolingo Noah learned
Spanish, Portuguese, French, and Irish well enough to engage in conversations,
and a bit of 11 other languages. Apart from Duolingo, he is also learning
Haitian/Creole using other web resources and with a friend at school who speaks
the language.
Factors that make Duolingo an effective
tool include its bite-sized assessment-as-learning lessons and continuous
game-like feedback. This is competency-based tutoring at its best. Learners
advance only after demonstrating mastery on granularly defined competencies, such
as translating a specific word or phrase. Feedback is instantaneous and focused
on correcting specific weaknesses. I see a lot of similarities between
principles within gamification and learning sciences, both draw from an
expanding knowledge of how the human brain develops and adapts to new
challenges. Game mechanics address learner motivation, providing the
right level of challenge at the right time (zone of proximal development),
building new knowledge/skills on existing knowledge/skills (constructivism),
goal setting and visibility into thinking and progress (learner agency).
To deliver this kind of experience for
the learner requires a rich set of data behind each assessment item (the
granular competency being assessed, what a correct or incorrect answer means
and what remedial feedback to give, etc.), detailed data collected every time
the learner attempts to answer to guide feedback and progress, and data about
the competencies and competency-based pathway.
“Big Data” and a Warning about
Learning Styles Data
“The theory of learning styles has been intensely reviewed,
tested and debunked,” but well meaning organizations still offer
learning style assessments and attempt to use the data to personalize learning.
Yes, big
data sets can be used by recommendation engines to help filter all
possible learning activities down to a few that are a good fit, just like
Google targets advertising and Amazon suggests products “you also might like.”
However, the notion that a person is a fixed type of learner that can be
classified using a one-time assessment is oversimplified. Preferences change
over time, the “best” instructional/study methods will vary based on context,
and students may need to try multiple modes of instruction (see a concept in
different ways) before mastering some learning objectives. It may be helpful
for a learner to think about what kind of learning mode they generally prefer,
but multiple options for each lesson allow the learner to choose how they right
now. Even Google search results give a list of options and let the user pick…I
don’t know anyone that regularly uses the “I’m feeling lucky” option.
The mode of presentation (visual,
auditory, kinetic, etc.) is just one of many variables factor into selecting a
learning activity. Being precise about the granular competency that the
learning activity addresses, and the quality of the resource, is more important
than the mode of presentation.
Analytics engines, informed by big data,
can do more than predict how well a learning activity will work for a student.
They can help create conditions for motivation and engagement to help the
learner reach personal goals.
Social Learning
Noah learns with friends on social media
including Google hangouts and Facebook language learners groups. He also seeks
out native speakers of the languages he is learning. When visiting the city
where a relative lives, he made it a point to walk into a Portuguese bakery and
start a conversation with the people working there.
Through school choice, he is attending a
high school outside of his home district and enrolled in a French class just to
get required credit for graduation, but he doesn’t think he’s learning anything
there that he has or could learn on his own initiative. And his friends on
social media are more at his level for conversations in French. So next
semester his high school teacher will create a special “French 5” independent
study option in which Noah will help teach French to freshmen.
Peer assessment can be an effective part
of DIY learning. For some subjects data may be collected with online
rubric-based peer assessment tools. Assessment-for-learning data is informs
feedback.
Data for Feedback
There are three levels of feedback to
support student-centered learning:
- Immediate feedback given during the learning activity after each click/response,
- Feedback at the end of a lesson that answers the question “What next?”
- Dashboards and progress maps that answer the question “How am I doing in reaching short and long-term goals?”
The 3rd kind of feedback allows learners
to carry out personal learning plans as a kind of GPS guiding them to
longer-term goals.
Data for Planning and
Decision-Making
DIY learners are motivated by a purpose.
Noah‘s fascination with linguistics motivated him to take ownership of his own
learning. That interest is leading to decisions about college and career.
Often the purpose for learning is to gain abilities needed to support a
cause, calling, or career goal. Noah sees himself pursuing a career as a
translator, but realizes that his interests and goals may change in the future.
Emerging sources of data will help DIY
learners map backwards to identify credentials needed to support cause or
career, and the competencies required to attain each credential. There is a
trend in higher education and workforce training to offer stackable credentials
such as a certificate that counts toward a degree. Projects such as the Credential Registry plan to provide data to help DIY learners
make informed decisions about long-term learning goals and alternative pathways
to reaching those goals.
The DIY learner then can track progress
toward goals with the right data about achievements. Most of the time progress
data is not in control of the learner and constrained to a specific context,
such as language learning data within Duolingo, mathematics data in Khan
Academy, course transcript data in a high school or college information system.
However, several initiatives are working to give students control of their
data. Initiatives like the Badge Alliance have published standards for the
data representing achievements, and other organizations are building on
previous work toward student-centered, secure, verifiable claims and
credentials.
Data about pathways, plans, and progress
can be combined and presented in a dashboard for the DIY learner. This is
already available within silos, but someday learners will be able to get a more
complete picture.
Finally, the same kind of “paradata” used
to rate quality and fit of individual learning resources can also be used to
inform bigger decisions, such as quality, fit, and cost-effectiveness of
college programs.
Now, Noah is considering a college that
has a large language department with a good reputation, but that doesn’t tell
him if the program is better than other options at preparing people to do what
he wants to do after college. It also doesn’t tell him if the program is the
most cost effective way of reaching his long-term goals. Some of this information
can be discovered/collected from unstructured data, e.g. within social media
and surveys. Other data might be generated through “big data” analytics.
(Existing “college recommendation engines” tend to be more about evaluating the
student’s chances of being accepted, rather than evaluating the value that a
college program offers its graduates.)
A Vocabulary for Talking about
GenDIY Education Data
The Common Education Data Standards (CEDS) defines
the meaning of data elements used to support DIY learning including data for
discovery of learning resources/opportunities, data used in
assessment-as-learning and intelligent tutoring systems, data for planning and
decision-making (including competency and credentials definitions, and
achievement tracking).
CEDS.ed.gov includes a searchable glossary of data “vocabulary” that
is aligned to many of the other standards mentioned in this article. Other
standards address the protocols and technical details for interoperability of
systems and content for each of the kinds of data.
