Towards new forms of knowledge communication: the adaptive dimension of a web-based learning environment

Papanikolaou, Kyparisia A.; Grigoriadou, Maria; Magoulas, George D.; Kornilakis, Harry

doi:10.1016/s0360-1315(02)00067-2

Cited by 123 publications

(74 citation statements)

References 7 publications

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“…Computer-based, adaptive learning systems are beginning to accommodate differences in learner interests, aptitudes, and background (e.g., Bajraktarevic, Hall, & Fullick, 2003;Conlan & Wade, 2004;De Bra et al, 2003;Papanikolaou, Grigoriadou, Magoulas, & Kornilakis, 2002;Weber & Brusilovsky, 2001).…”

Section: Tailored Contentmentioning

confidence: 99%

Tensions, Trends, Tools, and Technologies: Time for an Educational Sea Change

Shute¹

2006

ETS Research Report Series

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This report outlines three educational approaches: (a) traditional, the currently dominant approach, a largely lecture-oriented, authoritarian style that makes heavy use of assessments of learning, which are useful for accountability purposes but only marginally useful for guiding day-to-day instruction; (b) progressive, a highly student-centered approach that relies on assessments for learning, which can be very useful in guiding day-to-day instruction; and (c) unified, a new, integrated approach that uses the best of both kinds of assessments-for and of learning-and which leverages computer technology, educational measurement, and cognitive science to address factors that undermined earlier attempts to implement the progressive approach. This report examines some of the research, trends, and factors that should be considered, understood, and, in some cases, leveraged, in order to move toward the unified approach. Further, this report presents examples of how ETS projects are moving toward the new approach to harness assessment in the service of learning.

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Section: Tailored Contentmentioning

confidence: 99%

Tensions, Trends, Tools, and Technologies: Time for an Educational Sea Change

Shute¹

2006

ETS Research Report Series

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“…It is important to note that each student has his/her own characteristics (profile, learning style, prior background and learning objectives). These individual traits are very useful to provide each student the most adequate learning path to attain his/her learning outcomes (Garrido & Onaindia, 2013;Papanikolaoum, Grigoriadou, Magoulas, & Kornilakis, 2002). In other words, it is not enough to plan a general learning path for all students but to personalize as much as possible each learning path.…”

Section: Learning Management Systemsmentioning

confidence: 99%

Student-oriented planning of e-learning contents for Moodle

Caputi

Garrido

2015

Journal of Network and Computer Applications

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We present a way to automatically plan student-oriented learning contents in Moodle. Rather than offering the same contents for all students, we provide personalized contents according to the students' background and learning objectives. Although curriculum personalization can be faced in several ways, we focus on Artificial Intelligence (AI) planning as a very useful formalism for mapping actions, i.e. learning contents, in terms of preconditions (precedence relationships) and causal effects to find plans, i.e. learning paths that best fit the needs of each student. A key feature is that the learning path is generated and shown in Moodle in a seamless way for both the teacher and student, respectively. We also include some experimental results to demonstrate the scalability and viability of our approach.

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“…These include information dissemination, entertainment recommendations, search engines, medicine, tourism, financial services, consumer goods and e-learning (Adomavicius & Tuzhilin, 2005;Garcia-Crespo et al, 2011;Kim et al, 2009;Papanikolaou, & Grigoriadou, 2002).…”

Section: E-learning Recommender Systemsmentioning

confidence: 99%