Lydia Odilinye - Academia.edu (original) (raw)

Papers by Lydia Odilinye

Learning, an active cognitive activity, differs from one learner to another. This therefore sugge... more Learning, an active cognitive activity, differs from one learner to another. This therefore suggests the need for personalized learning. Recommender systems in this context can be seen as a resourceful tool to provide appropriate learning materials that are tailored to the (personalized) learning needs and goals of the learner; and also to enhance learning. The development of personalized recommender systems typically involves a learner model component, which is used to capture and store the personal information, preferences and other characteristics of the learner. While reading, learners engage in number of metacognitive activities e.g. text marking/creating highlights. These metacognitive interactions could serve as useful information for the learner model, to achieve personalization. In addition, the use of a probabilistic topic modeling based document retrieval (Latent Dirichlet Indexing) method makes it possible to provide finer grained multiple but topically related documents...

Personalized Recommender System Using Learners' Metacognitive Reading Activities

Learning, an active cognitive activity, differs from one learner to another, suggesting the need ... more Learning, an active cognitive activity, differs from one learner to another, suggesting the need for personalized learning. The development of personalized recommender systems typically involves a learner model component, which is used to capture and store the personal information, preferences and other characteristics of the learner. While reading, learners engage in number of metacognitive activities e.g. text marking/highlights. These metacognitive interactions could serve as useful information for the learner model, to achieve personalization. The recommender system developed is integrated with nStudy, an online learning platform that provides a number of annotation tools (e.g. highlighting, tags) that support metacognitive activities. A user study was conducted to evaluate the effectiveness of using the highlights (a metacognitive activity) a learner makes while reading, as a preference elicitation method for the learner model. The findings show that the learner generated metac...

Yorùbá being a tone language requires tone information for the correct pronunciation of words in ... more Yorùbá being a tone language requires tone information for the correct pronunciation of words in Text-to-Speech synthesis. Based on standard Yorùbá orthography, such information is held in tone marks, which applied to vowels and syllabic nasals as diacritical markings. However, the tone marks are not always correctly applied in many Yorùbá documents because appropriate input devices for the accurate application of the diacritic marks are not always available. Hence, the absence of tone marks in most written Yorùbá texts presents a major challenge in speech synthesis as the information required for applying the right tone sequences to synthesized Yorùbá speech may not always be available. This study proposes the use of Machine Learning techniques as a basis for the automatic application of tone marks as part of the pre-processing in high level synthesis. Being a resource-scarce language however, there is a lack of sufficiently large Yorùbá corpora for the training of an automatic dia...

Quantifying the effect of corpus size on the quality of automatic diacritization of Yorùbá texts

Aligning automatically generated questions to instructor goals and learner behaviour

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), 2015

Quantifying the effect of corpus size on the quality of automatic diacritization of Yorùbá texts

Learning, an active cognitive activity, differs from one learner to another. This therefore sugge... more Learning, an active cognitive activity, differs from one learner to another. This therefore suggests the need for personalized learning. Recommender systems in this context can be seen as a resourceful tool to provide appropriate learning materials that are tailored to the (personalized) learning needs and goals of the learner; and also to enhance learning. The development of personalized recommender systems typically involves a learner model component, which is used to capture and store the personal information, preferences and other characteristics of the learner. While reading, learners engage in number of metacognitive activities e.g. text marking/creating highlights. These metacognitive interactions could serve as useful information for the learner model, to achieve personalization. In addition, the use of a probabilistic topic modeling based document retrieval (Latent Dirichlet Indexing) method makes it possible to provide finer grained multiple but topically related documents...

Personalized Recommender System Using Learners' Metacognitive Reading Activities

Learning, an active cognitive activity, differs from one learner to another, suggesting the need ... more Learning, an active cognitive activity, differs from one learner to another, suggesting the need for personalized learning. The development of personalized recommender systems typically involves a learner model component, which is used to capture and store the personal information, preferences and other characteristics of the learner. While reading, learners engage in number of metacognitive activities e.g. text marking/highlights. These metacognitive interactions could serve as useful information for the learner model, to achieve personalization. The recommender system developed is integrated with nStudy, an online learning platform that provides a number of annotation tools (e.g. highlighting, tags) that support metacognitive activities. A user study was conducted to evaluate the effectiveness of using the highlights (a metacognitive activity) a learner makes while reading, as a preference elicitation method for the learner model. The findings show that the learner generated metac...

Yorùbá being a tone language requires tone information for the correct pronunciation of words in ... more Yorùbá being a tone language requires tone information for the correct pronunciation of words in Text-to-Speech synthesis. Based on standard Yorùbá orthography, such information is held in tone marks, which applied to vowels and syllabic nasals as diacritical markings. However, the tone marks are not always correctly applied in many Yorùbá documents because appropriate input devices for the accurate application of the diacritic marks are not always available. Hence, the absence of tone marks in most written Yorùbá texts presents a major challenge in speech synthesis as the information required for applying the right tone sequences to synthesized Yorùbá speech may not always be available. This study proposes the use of Machine Learning techniques as a basis for the automatic application of tone marks as part of the pre-processing in high level synthesis. Being a resource-scarce language however, there is a lack of sufficiently large Yorùbá corpora for the training of an automatic dia...

Quantifying the effect of corpus size on the quality of automatic diacritization of Yorùbá texts

Aligning automatically generated questions to instructor goals and learner behaviour

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), 2015

Quantifying the effect of corpus size on the quality of automatic diacritization of Yorùbá texts