TY - GEN
T1 - Evaluating the impact of personalized content recommendations on informal learning from wikipedia
AU - Ismail, Heba
AU - Belkhouche, Boumediene
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Information wikis and especially Wikipedia are attracting an increasing attention for informal learning. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. To the best of our knowledge, no effective personalized content recommendation approach has yet been defined to support informal learning from wikis. Therefore, we propose a personalized content recommendation framework that extrapolates topical navigation graphs from learners' free navigation and integrates them with fuzzy thesauri for automatic and adaptive personalized content recommendations to support informal learning in wikis. We design user studies and conceptual knowledge rubric to evaluate the impact of personalized recommendations on learning from Wikipedia. Results show that the proposed personalized content recommendation framework generates highly relevant recommendations. Evaluation of informal learning reveals that users who use Wikipedia with personalized recommendations can achieve higher scores on conceptual knowledge assessment compared to those who use Wikipedia without recommendations. Learners who use Wikipedia with personalized recommendations are able to utilize larger number of concepts and are able to make comparisons and state relations between concepts.
AB - Information wikis and especially Wikipedia are attracting an increasing attention for informal learning. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. To the best of our knowledge, no effective personalized content recommendation approach has yet been defined to support informal learning from wikis. Therefore, we propose a personalized content recommendation framework that extrapolates topical navigation graphs from learners' free navigation and integrates them with fuzzy thesauri for automatic and adaptive personalized content recommendations to support informal learning in wikis. We design user studies and conceptual knowledge rubric to evaluate the impact of personalized recommendations on learning from Wikipedia. Results show that the proposed personalized content recommendation framework generates highly relevant recommendations. Evaluation of informal learning reveals that users who use Wikipedia with personalized recommendations can achieve higher scores on conceptual knowledge assessment compared to those who use Wikipedia without recommendations. Learners who use Wikipedia with personalized recommendations are able to utilize larger number of concepts and are able to make comparisons and state relations between concepts.
KW - Content recommendations
KW - Informal learning
KW - Personalized content recommendations
KW - Structural recommendations
KW - Wikipedia
KW - Wikis
UR - http://www.scopus.com/inward/record.url?scp=85067515939&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067515939&partnerID=8YFLogxK
U2 - 10.1109/EDUCON.2019.8725052
DO - 10.1109/EDUCON.2019.8725052
M3 - Conference contribution
AN - SCOPUS:85067515939
T3 - IEEE Global Engineering Education Conference, EDUCON
SP - 943
EP - 952
BT - Proceedings of 2019 IEEE Global Engineering Education Conference, EDUCON 2019
A2 - Schreiter, Sebastian
A2 - Ashmawy, Alaa K.
PB - IEEE Computer Society
T2 - 10th IEEE Global Engineering Education Conference, EDUCON 2019
Y2 - 9 April 2019 through 11 April 2019
ER -