Learning Analyticsの定義 “Learning analytics is the measurement, collecti on, analysis and reporting of data about learner s and their contexts, for purposes of understand ing and optimizing learning and the environmen ts in which it occurs. “ Ferguson, R. (2012). Learning analytics: drivers, developments and chal enges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317. (LAK 2011) 3
教育の情報化におけるLAの位置付け Delivery Server School Server LMS LRS (Learning Learning (Publisher) (Campus) （Material/Quiz） Record Store) Analytics Server Ups p t s r t e r am a Mids d t s r t e r am a Do D w o ns n t s r t e r am a = Delive v ry r = Le L ar a n r ing = Ana n lyti y cs School Learner 4
LA Overview School LMS M /VL /V E Feedback Assessment/ Feedback/ to Institutional Edu d u Ap A ps p Adaptive Teacher Research Navigation 3rd 3r d P art ar y A y pps p Learn ar er Teacher Profile Pro r file Profile Memory Data Store / Output / Data Dat C a o C l o llect ec i t o i n o Data Store / Analysis Output / Fi F lit l e t ri r ng n Analysis Feedba F ck eedba xAPI Data Exchange Statistics Visualization IMS Caliper Pre-process Big Data Analysis Dashboard Adaptive Data (ex. NLP) Artificial Intelligence Recommendation Collector Adaptation E-Portfolio Secured Data Exchange Revised from ISO/IEC TR20748-1 Learning Analytics Interoperability: Reference Model 5
Learning Analyticsの入力と出力 Input Process/ Outcomes Goals Data Items Model Material Personalization Access 学習進捗 学習 の 進捗 進捗 Individual Quiz Answer Learner 解答の 解 正誤判定 正誤判 Intervention Support 学習へ 学習 の割り 割 込み り St S atist s ic i s c , s Discussion Bi B g da
t da a a ナビゲ ビ ーショ ーシ ン ョ anal an ys al i ys s i ： Visualization 教員評価 Prior Credits Institutional 学校 学 の 校 評価 Research 学校 学 と 校 して し の問題点 Achieve- の抽出 Prediction の抽 ment 文部科学 文 省 部科学 諸外 省 国 諸外 に 国 お に ける お 学 ける 校 評価等の 評価 状況 等の h 状況 tt h p: tt // / g / o g o o . o gl g /7 / h 7 y h X y pS X 7
先行研究におけるLAの入力⇔出力の例 Table 1. Data items and Objectives of Learning Analytics Researches Table 2. Data items and Objectives of Learning Analytics Researches (classroom and individual learning). (Collaborative and active learning).
Reference Data Items Goal of Analysis Reference Data Items Goal of Analysis Arnold and Posting of a traffic signal indicator on a Relationship between items and Ahn (2013) Emails received, Emails sent, Friends, Friend Factor analysis of media literacy Pistilli (2012) student's LMS home page, E-mail messages achievement Lists, Links, Member pages, Networks, (Negotiation, Networking, or reminders, Text messages, Referral to academic advisor or academic resource Notes, Photos, Status messages, Videos, Wall Judgment, Play, Multitasking, center, Face to face meetings with the posts Appropriation, Transmedia instructor navigation) Barber and Prior credits earned, Discussion post Prediction of class achievement Cambridge Discussion post, blog, their narratives, Analysis of discourse style to Sharkey count/week, Late assignments, Orientation and Perez- activate learner groups (2012) participation, Count of messages to Lopez (2012) instructor, Inactive time since last course Clow (2013) Visit, Registration, and contribution ratio of Drop rate analysis of MOOCs Camilleri et Pleases and numbers of utterances in virtual Behavior analysis MOOCs learners al. (2013) space Graf et al. Templates, patterns, learning object, Judgment of material difficulty Cobo et al. Reading and writing activities during online Clustering of learners (2011) database connections of materials (2012) discussions Holman et al. Grade, Class standing, and badges of Self prediction of achievement Ferguson and Keywords in text chat Chat type (evaluation, (2013) quizzes Shum (2011) explanation, reasoning, Kizilcec et al. Visiting, Enrollment, and assessment Number transition of MOOCs (2013) numbers in MOOCs courses learners justification, perspective) Lonn et al. Grade information every few weeks Assistance necessity from mentors Koulocheri Bookmarks, blog posts, topics and files Visualization of member (2012) and Xenos uploaded, bookmarks, comments on relationships Martin et al. Answers of each sub-quiz Visualization of learning process (2013) bookmarks/blog posts/topics/files in group (2013) De Liddo et Response type of utterances (respond, about, Relationship analysis of learners Monroy et al. Teacher’s usage of teaching unit parts Heat map of unit parts usage al. (2011) example, solution, support) (2013) (overview, essentials, engage, explore, Schneider et Eye-tracking data Estimation of collaborative explain, evaluate, intervention, acceleration) al. (2013) learning skills Niemann et al. Learning object usage in a web portal Similarity of learning objects Schreurs et Person, type of tie, topic Visualization of learner (2012) al. (2013) relationship network Pardos et al. Quizzes and scaffolding help Relationship between Scaffolding Shum and Quiz achievement and various activities Relationship between individual (2013) help and achievement Crick (2012) learning achievement and Raca and Video captured actions of learners Learner behavior during classrooms meta-skills Dillenbourg (2013) Siadaty et al. Vocabulary in shared Wiki and bookmark Collaborative skills analysis of Santos et al. Date and time range of learners Visualization of learning status (2012) corporate learners (2012) Suthers and Chat, Discussion, File sharing Multiple level visualization Sao Pedro et Quiz answers Transition of problem solving skills Rosen (2011) (Process, Domain, Event, Action, al. (2012) Mediation, Relationship, Tie) Tempelaar et Achievements in various learning areas Skill analysis (Self-belief, learning Tempelaar et Achievements in various learning areas Analysis of necessary skills al. (2013) focus, planning, management, persistence) al. (2013) (Self-belief, learning focus, Verbert and Dataset and functions of recommender Comparison of Recommender planning, management, Duval (2011) system systems persistence) Wolff and Precision and recall of learning units Comparison of TMA
Zdrahal (2013) (Tutor-marked assessment) and VLE (Virtual learning environment)
ISO/IEC JTC1/SC36 WG8 について • SC36 – Information Technology for Learning, Education an d Training (ITLET) – 1999年発足、2000年初会合 (London) • 2015年6月 WG8 (Learning Analytics) が発足 – Convener(議長)：Yong-Sang Cho (KERIS) – ISO/IEC TR 20748 • WG8/N0010: Reference Model • WG8/N0011: System Requirements – Study Group • Systems governance for learning analytics • Data framework for learning analytics interoperability 12
The IMS Learning Analytics group wil leverage and incorporate, into the Caliper Metrics Profiles, ongoing efforts by various organizations to define metrics and/or desired learning and developmental outcomes for various aspects of the edu-graph. These include (but are not limited to):
The Predictive Analytics Reporting (PAR) framework’s Data Models as part of IMS Context and Engagement metrics; Common Core Standards as part of IMS Performance metrics; CAS standards for student developmental and learning outcomes for higher education as part of IMS Performance metrics.