DocumentCode
41833
Title
Individualization for Education at Scale: MIIC Design and Preliminary Evaluation
Author
Brinton, Christopher G. ; Rill, Ruediger ; Sangtae Ha ; Mung Chiang ; Smith, Robert ; Ju, William
Author_Institution
Dept. of Electr. Eng., Princeton Univ., Princeton, NJ, USA
Volume
8
Issue
1
fYear
2015
fDate
Jan.-March 1 2015
Firstpage
136
Lastpage
148
Abstract
We present the design, implementation, and preliminary evaluation of our Adaptive Educational System (AES): the Mobile Integrated and Individualized Course (MIIC). MIIC is a platform for personalized course delivery which integrates lecture videos, text, assessments, and social learning into a mobile native app, and collects clickstream-level behavioral measurements about each student as they interact with the material. These measurements can subsequently be used to update the student´s user model, which can in turn be used to determine the content adaptation. Recruiting students from one of our Massive Open Online Courses (MOOCs), we have conducted two preliminary trials with MIIC, in which we found (i) that the majority of students (70 percent) preferred MIIC overall to a one-size-fits-all (OSFA) presentation of the same material, (ii) that the mean level of engagement, when quantified as the number of pages viewed, was statistically higher (by 72 percent) among students using MIIC than among OSFA, and (iii) that the integrated multimedia learning features were generally favorable among the students (e.g., 87 percent found the videos helpful).
Keywords
computer aided instruction; educational administrative data processing; educational courses; mobile computing; AES; MIIC design; MOOC; OSFA presentation; adaptive educational system; assessment; clickstream-level behavioral measurement; education at scale; lecture video; massive open online courses; mobile integrated and individualized course; mobile native app; multimedia learning feature; one-size-fits-all presentation; personalized course delivery; preliminary evaluation; social learning; student user model; text; Adaptation models; Adaptive systems; Education; Materials; Mobile communication; Navigation; Videos; Adaptive Educational Systems; Individualization; MOOC; Personalized Learning; Personalized learning; adaptive educational systems; individualization; online learning;
fLanguage
English
Journal_Title
Learning Technologies, IEEE Transactions on
Publisher
ieee
ISSN
1939-1382
Type
jour
DOI
10.1109/TLT.2014.2370635
Filename
6955856
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