AIR Forum 2019
May 28-31, 2019
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5/29 (WED)
参加したセッション
5/29 (WED)
Breakfast
30 countries
50 states
1900+ attendees
https://gyazo.com/6f7112cde9a78c6dc011146790f16e22
What Big Data Doesn’t Tell You: The Pitfalls of Data-Driven Decision Making
Keynote
Tricia Wang, a global tech ethnographer
Why do so many organizations make bad decisions, even with access to unprecedented amounts of data? With stories from Nokia to Netflix to the oracles of ancient Greece, Tricia Wang demystifies big data and identifies its pitfalls, suggesting that we focus instead on "thick data" — precious, unquantifiable insights from actual people — to make the right business decisions and thrive in the unknown. To effectively leverage big data, we need insights that come from both quantitative and qualitative data.
companyはinnovationではsucceedするが、decision makingにはmiserably failedしている
precious data from humans that captures their emotions and stories
quantification bias
the unconscious belief of valuing the measurable over the immeasurable
How to get out of quantification bias
https://gyazo.com/d5471bf50fed7aff45533e747915c317
more data でなく、more communicationがbetter decision makingにつながる
Time-to-Degree Analysis Made Easy with Tableau (Speaker Session)
Time-to-Degree:
Number of calender years that elapsed between the start of a student's first term and graduation
Time-to-Degreeの分布をさまざまな切り口で可視化（Tableauで）
Culturally Engaging Campus Environments (CECE) and Diverse Student Success (Speaker Session)
CECE is to understand and measure various aspects of the campus environment that maximized success among all students
This study examines the relationship between CECE and a sense of belonging for Black and Latinx students and investigating gender gap CECE may be positively related to students' outcomes
5/30 (THU)
From IE Framework to IT Dashboard: Process for a Successful Partnership (Speaker Session)
input-output-outcome-impactの4つの切り口でinstitutional effectivenessを評価するフレームワーク
input
What we have, our resources
curriculum. coursem and assessment design
faculty attributes
learner readiness
academic and co-curricular resources
financial and staffing resources
output
What we do, our activities
teaching and assessment effectiveness
doctoral mentoring effectiveness
advising effectiveness
learner satisfaction & engagement
outcome
How learners change, KASB)
learner competencies
learner progression
learner completion
impact
How the communuity changes
learners advance professionally
employers grow and communities benefit
economies prosper
各項目に基準を定めて青・黃・赤のsignalで状態を表示するダッシュボードを作っている
Leveraging Student Flow Analysis to Improve Retention and Student Success (Speaker Session)
University at BuffaloのInstitutional Analytics Team
活動内容やダッシュボードの例など
pipeline（学生のエンロールメントのフロー）をTableauで可視化した例
Hidden Figures: Course Withdrawals as an Indicator of Student Distress (Speaker Session)
成績「W (withdraw)」をちゃんと分析すべき、という内容
WはGPAに反映されないが、Wの多寡は学生のdistressの状態を反映している
Utilizing Neural Networks to Estimate First-Year Student Retention (Speaker Session)
リテンション予測にニューラルネットワークを用いた例
ニューラルネットワークの使い方紹介のような側面もあった
(SAIR Best Presentation) Predicting Student Enrollment Using Markov Chain Modeling in SAS (Speaker Session)
各学年の学生数の推移を、学年間の推移確率としてモデル化して、在籍数等を予測するといった感じ。やや単純か…
Early Intervention: Identify At-Risk Students Using Canvas LMS and Tableau (Speaker Session)
LMSログからわかる、学生の活動のようすとパフォーマンスは明らかに相関する
DFW（成績D, F, W） rateのモニタリングや、at-risk studentsの割り出しなど （この発表とは関係ないけど参考）
FGUのIRオフイスのページ
metricsの定義
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会場のようす
https://gyazo.com/36b4e6deeaef3fa5265bd797fa5dbf91
https://gyazo.com/1b607d8794f952a5480c75143e136afa
https://gyazo.com/b70533e6370891fb1657848b2dd206fc
https://gyazo.com/7567c4e65fe8170af7098d6d5f7027bb
https://gyazo.com/3d55af88b982100ccbd98a82d0232887
https://gyazo.com/52f64cc61ee8e159a36e0453b3ea8c4e
https://gyazo.com/8c4e125edec6597c6faf59d80d66cc5b
来年（2020年）はニューオーリンズ！
ちなみに2021年はワシントンDC