Rohail Syed wins best student paper award at CHIIR
Rohail Syed, a PhD student at the University of Michigan School of Information, has won a Best Student Paper award for his work on a study examining how online Web search can be enhanced to support self-directed learning.
The award for “Exploring Document Retrieval Features Associated with Improved Short- and Long-term Vocabulary Learning Outcomes” was announced at the annual ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR), held last week in New Brunswick, NJ.
Syed has been working on his PhD research with Kevyn Collins-Thompson, associate professor at UMSI and U-M’s College of Engineering; he and Syed are co-authors of the paper.
Collins-Thompson applauded this recognition of his colleague’s work.
"Rohail is an exceptional researcher and working with him has been a real highlight of my academic career,” he said. “It's wonderful to see his creativity and hard work get the special recognition it deserves from this highly competitive award at an outstanding venue like the ACM CHIIR conference."
“I was surprised and very pleased to win this award,” Syed said, “and I appreciate Kevyn’s help throughout my academic study here at UMSI.”
This is the second study the two have co-authored that examines ways to tailor online searches to enhance learning. The first study analyzed how an effective algorithm for online search engines could enhance short-term learning outcomes.
The second study specifically looked at how features of documents and user knowledge relate to multiple types of learning, and in particular long-term retention, as a result of document search using a “personalized learning-oriented retrieval algorithm,” according to Syed. “This is, to our knowledge, the first crowdsourced, longitudinal study of long-term retention.”
Both studies focused on vocabulary learning and retention. Various features of Web pages, including patterns and density of keyword use, the existence of supporting images, and the readability of surrounding text, were used to estimate participants’ likely short-term and long-term learning retention.
Syed and Collins-Thompson say this is a fruitful, ongoing area for research. After all, when it comes to learning, online searches are integral – for everyone, from elementary school students to professionals.
Helping this task to evolve into an effective learning tool makes sense. But people have different learning styles, thus the need for a personalized-learning approach to retrieval.
Next, Syed said, “is to construct models that are fairly easy to implement and could generalize to multiple learning styles.”
Such a model “could be implemented by existing search engines, organizations and individuals.”
by Sheryl James, UMSI PR Specialist
Posted March 20, 2018