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Courseware in Higher
Education
Effectiveness and Salient Trends
By: Fengfeng Ke, [email protected]
Literature on Courseware in Higher Education (2005-2015)
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Courseware in Higher Education
 Interactive and hybrid learning
 MOOCs
 Intelligent tutoring systems
 Adaptive and interactive multimedia
 Games
 Educational data mining and learning analytics
 Immersive learning systems
Interactive and hybrid learning
 Design and evaluation of an online or hybrid e-learning
 Higher course completion rate among the students assigned to the hybrid-format
section, in comparison with students in the traditional-format section (Bowen,
Chingos, Lack, & Nygren, 2014).
 The odds of graduating increase for students with distance education coursework
in the first year of study (Shea & Bidjerano, 2014).
 Recent focus on adaptive, personalized e-learning system development
MOOCs
 Open Courseware (OCW) and Massive Open Online Course (MOOC) movements
 Empirical research is limited
 No significant difference between students in MOOCs and students in traditional
sections in terms of pass rates, scores on common assessments, and grades
(Griffiths, Chingos, Mulhern, & Spies, 2014).
 Most MOOCs’ instructional deign quality is low (Sinclari, Boyatt, Rockes, & Joy,
2014).
Intelligent Tutoring Systems
 Meta-analyses (Kulik & Fletcher, 2015; Ma, Adesope, Nesbit, & Liu, 2014; Nye,
Graesser, & Hu, 2014; Steenbergen-Hu & Cooper, 2014; VanLehn, 2011)
consistently reported a moderate to large positive effect of intelligent tutoring
systems on students’ learning outcomes in comparison with conventional,
teacher-led instruction and non-ITS computer-based instruction.
 Such an effect is consistently found in varied implementation settings, for all
levels of education and diverse subject domains.
Adaptive and Interactive Multimedia
 Ranging from non-interactive lecturing video and audio (e.g., podcasting),
interactive simulation and pedagogical agent, to digital learning game and 3D
web application
 Preliminary findings support the learning effectiveness of adaptivity (in content
presentation and guidance).
Educational Data Mining and Learning Analytics
 Data mining and learning analytics as means to examine methods of learner
assessment and creating dynamic learner support
 Data mining: Automated methods for discovery within educational data for
automated adaption
 Learning analytics: Human-led methods for exploring educational data to provide
instructor support
 Current research on educational data mining and learning analytics focuses on
development-based product evaluation
 Course Signals (CS) at Purdue University
(http://www.itap.purdue.edu/learning/tools/signals/)
Games and Immersive Learning Systems
 Digital games improved students’ learning outcomes relative to typical
instruction conditions (Clark, Tanner-Smith, & Killingsworth, 2015)
 Immersive, embodied condition consistently led to greater learning gains,
compared to regular instruction (Johnson-Glenberg, Birchfield, Tolentino, &
Koziupa, 2014)
Areas in Need of Critical Research for Courseware in HE
Systematic
research
• Longitudinal
• Cross-site
• Rigorous
Adaptivity
Model
Goal
• Multidisciplinary
• Data-driven,
multi-level
adaptivity
• Design model
• Technology-rich
pedagogy
training
• Supplementary
vs. Change
agent
• Segmented
initiatives