E ffective teachers do no simply present information for students to learn by rote for a good grade. Rather, they provide an environment in which students can engage with course material and both learn and interpret it for their own interest and goals. With this in mind, I have spent a considerable amount of time developing an active learning format for my introductory biology course, including...

The PHLIpS (Producing Hands-on Learning to InsPire Students) Method provides a systematic approach for professors desiring to develop active learning activities for their classrooms. Many professors appreciate the benefits of active learning and wish to include more activities in their classroom. Unfortunately, activities for a given topic or course are commonly not available as an off-the-self...

We present an unsupervised learning method for disambiguating verbs that belong to more than one Levin verb class (1993) when occurring in a particular syntactic frame. We used examples that contain unambiguous verbs in each verb class as the training data for ambiguous verbs in that class. A Naive Bayesian classifier was employed for the disambiguation task using context words as features. Our...

This research shows that active learning is not universally effective and, in fact, may inhibit learning for certain types of students. The results of this study show that as increased levels of active learning are utilized, student test scores decrease for those with a high grade point average. In contrast, test scores increase as active learning is introduced for students in the lower level g...

This paper introduces a novel unsupervised constraint-driven learning algorithm for identifying named-entity (NE) transliterations in bilingual corpora. The proposed method does not require any annotated data or aligned corpora. Instead, it is bootstrapped using a simple resource – a romanization table. We show that this resource, when used in conjunction with constraints, can efficiently ident...

Traditional multiple kernel learning (MKL) algorithms are essentially supervised learning in the sense that the kernel learning task requires the class labels of training data. However, class labels may not always be available prior to the kernel learning task in some real world scenarios, e.g., an early preprocessing step of a classification task or an unsupervised learning task such as dimens...

Supervised learning is a powerful technique to build models that can associate data to given targets. Today this is a successful method that is widely adopted in the industry. However, supervised learning comes with limitations: It relies on costly, timeconsuming and error-prone manual labeling of a large set of examples. Moreover, animals do not seem to learn about objects through a teacher du...

Representation learning is one of the foundations of Deep Learning and allowed big improvements on several Machine Learning fields, such as Neural Machine Translation, Question Answering and Speech Recognition. Recent works have proposed new methods for learning representations for nodes and edges in graphs. In this work, we propose a new unsupervised and efficient method, called here Neighborh...