The science of teaching scientists scientific software development for science and data science
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Transcript of The science of teaching scientists scientific software development for science and data science
Scientists learning software / Christopher Beitel / Mozilla Brown Bag, 2014
The science of teaching scientists software development for science and data science
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Christopher Beitel, Ph.D. Candidate, UC Davis
@datascimed
Scientists learning software / Christopher Beitel / Mozilla Brown Bag, 2014 2
Motivation for goals
Skill goals
7 Principles of learning
Discussion
The outline of the talkSo you don’t feel lost
Scientists learning software / Christopher Beitel / Mozilla Brown Bag, 2014 3
The concept of resource non-infinitenessAll the money < infinity
Scientists learning software / Christopher Beitel / Mozilla Brown Bag, 2014 4
Software education?Self-teaching appears to leave a gap in knowledge
90%Primarily self-taughtLearning with little or no guidance
47%Understand testingWhich is concerning
65%Of medical studies were inconsistent when re-tested
30%Or more of scientists time spent developing software
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Let’s make one of theseThe graduate student or post-doc everyone wants
Uses best-practices and knows relevant advanced python libraries, data structures, algorithms, etc.
Python, Unix, and DB
Git, communication, lean/agile experience, task management, teaches others
Collaboration ready
Genomics, astro, etc. require advanced understanding of the material for that area.
Domain-specific knowledge
Value grows quickly over time as they constantly add new skills and bring these back to the research team
Constant self-directed learning
✓ Has a special place in their heart for whiteboards
✓ Highly integrated knowledge DB for fast lookup
✓ Takes on suitable challenges
✓ Makes positive assumptions about team members
✓ Seeks out assistance when necessary
✓ Highly motivated and expects to succeed
✓ Mentors and is mentored by others
✓ Helps maintain a constructive professional environment
General Features
JohnCreek@Github, please don’t sue
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✒ How people learnThe same basic ways everyone acquires new skills
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Key learning conceptsWhat we’ll be discussing
1 Prior knowledge
2 Knowledge organization
3 Motivation
4 Mastery
5Goal-directed practice + feedback
6Learning environment
7Self-direction
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Prior knowledgeLearning happens in the context of previous learning
There is widespread agreement among researchers that students must connect new knowledge to previous knowledge in order to learn (Bransford & Johnson, 1972; Resnick, 1983).
“”(Ambrose et al., 2010)
Diagnostic + self assessment
Mentor / student learning
Concept mapping + whiteboard
Intentionally link to prior knowledge
Scientists learning software / Christopher Beitel / Mozilla Brown Bag, 2014 9
Knowledge organizationThe brain is a database, choose your structure
If students lack a strongly connected network, their knowledge will be slower and more difficult to retrieve (Bradshaw & Anderson, 1982; Reder & Anderson, 1980; Smith, Adams, & Schorr, 1978).
“”(Ambrose et al., 2010)
Organize intentionally
Find extra connections
Find deeper themes
Concept map with an expert
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MotivationPersonal investment directs and sustains learning
Relevance to your life and career
Learn with real-world tasks
Appropriate level of challenge
Rely on support structure
… concepts that are central to understanding motivation: (1) the subjective value of the goal and (2) the expectations for successful attainment… (Atkinson, 1957, 1964; Wigfield & Eccles, 1992, 2000)
“”(Ambrose et al., 2010)
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MasteryWhen you are fluent with a skill
Identify component skills
Practice integration to reduce load
Practice right task, right tool
Repeated practice builds fluency
To achieve mastery … develop a set of key component skills, practice them to the point where they can be combined fluently, and know when and where to apply them appropriately.
“”(Ambrose et al., 2010)
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Goal-directed practice and feedbackTargeted practice and targeted feedback from experts
Specific, attainable goals for practice
Frequent practice over long term
Group, mentor, peer feedback
Feedback should identify patterns
Research shows that the amount of time someone spends in deliberate practice is what predicts continued learning in a given field, rather than time spent in more generic practice (Ericsson, Krampe, & Tescher-Romer, 2003).
“”(Ambrose et al., 2010)
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Learning environmentPeople can fulfill their potential when their needs are met
Model verbally, lead by example
Assumptions => verbal/non cues
Self-esteem and expectations
Inclusion and belonging
A negative environment may impede learning and performance, but a positive climate can energize students learning (Pascarella & Terenzini, 1991).
“”(Ambrose et al., 2010)
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Self-directed learningIt’s a skill to be learned, practiced, and taught
Assess the task given goals
Design & continuously test approach
Plan approach
Learn from expert autodidacts
… Learners need to engage in a variety of processes to monitor and control their learning (Zimmerman, 2001). … these models take the form of a cycle.
“”(Ambrose et al., 2010)
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Summary and discussionA recap of what we covered
What skills should a scientist who works with data have? !
Do you learn independently or in groups? !
Self-directed learning strategies? !
How explicitly do you think about learning?
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Thank you for having me
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@datascimedthat’s my twitter