Matching Items (3)
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- All Subjects: Information retrieval
- Creators: Bansal, Srividya
- Resource Type: Text
Description
Academia is not what it used to be. In today’s fast-paced world, requirements
are constantly changing, and adapting to these changes in an academic curriculum
can be challenging. Given a specific aspect of a domain, there can be various levels of
proficiency that can be achieved by the students. Considering the wide array of needs,
diverse groups need customized course curriculum. The need for having an archetype
to design a course focusing on the outcomes paved the way for Outcome-based
Education (OBE). OBE focuses on the outcomes as opposed to the traditional way of
following a process [23]. According to D. Clark, the major reason for the creation of
Bloom’s taxonomy was not only to stimulate and inspire a higher quality of thinking
in academia – incorporating not just the basic fact-learning and application, but also
to evaluate and analyze on the facts and its applications [7]. Instructional Module
Development System (IMODS) is the culmination of both these models – Bloom’s
Taxonomy and OBE. It is an open-source web-based software that has been
developed on the principles of OBE and Bloom’s Taxonomy. It guides an instructor,
step-by-step, through an outcomes-based process as they define the learning
objectives, the content to be covered and develop an instruction and assessment plan.
The tool also provides the user with a repository of techniques based on the choices
made by them regarding the level of learning while defining the objectives. This helps
in maintaining alignment among all the components of the course design. The tool
also generates documentation to support the course design and provide feedback
when the course is lacking in certain aspects.
It is not just enough to come up with a model that theoretically facilitates
effective result-oriented course design. There should be facts, experiments and proof
that any model succeeds in achieving what it aims to achieve. And thus, there are two
research objectives of this thesis: (i) design a feature for course design feedback and
evaluate its effectiveness; (ii) evaluate the usefulness of a tool like IMODS on various
aspects – (a) the effectiveness of the tool in educating instructors on OBE; (b) the
effectiveness of the tool in providing appropriate and efficient pedagogy and
assessment techniques; (c) the effectiveness of the tool in building the learning
objectives; (d) effectiveness of the tool in document generation; (e) Usability of the
tool; (f) the effectiveness of OBE on course design and expected student outcomes.
The thesis presents a detailed algorithm for course design feedback, its pseudocode, a
description and proof of the correctness of the feature, methods used for evaluation
of the tool, experiments for evaluation and analysis of the obtained results.
are constantly changing, and adapting to these changes in an academic curriculum
can be challenging. Given a specific aspect of a domain, there can be various levels of
proficiency that can be achieved by the students. Considering the wide array of needs,
diverse groups need customized course curriculum. The need for having an archetype
to design a course focusing on the outcomes paved the way for Outcome-based
Education (OBE). OBE focuses on the outcomes as opposed to the traditional way of
following a process [23]. According to D. Clark, the major reason for the creation of
Bloom’s taxonomy was not only to stimulate and inspire a higher quality of thinking
in academia – incorporating not just the basic fact-learning and application, but also
to evaluate and analyze on the facts and its applications [7]. Instructional Module
Development System (IMODS) is the culmination of both these models – Bloom’s
Taxonomy and OBE. It is an open-source web-based software that has been
developed on the principles of OBE and Bloom’s Taxonomy. It guides an instructor,
step-by-step, through an outcomes-based process as they define the learning
objectives, the content to be covered and develop an instruction and assessment plan.
The tool also provides the user with a repository of techniques based on the choices
made by them regarding the level of learning while defining the objectives. This helps
in maintaining alignment among all the components of the course design. The tool
also generates documentation to support the course design and provide feedback
when the course is lacking in certain aspects.
It is not just enough to come up with a model that theoretically facilitates
effective result-oriented course design. There should be facts, experiments and proof
that any model succeeds in achieving what it aims to achieve. And thus, there are two
research objectives of this thesis: (i) design a feature for course design feedback and
evaluate its effectiveness; (ii) evaluate the usefulness of a tool like IMODS on various
aspects – (a) the effectiveness of the tool in educating instructors on OBE; (b) the
effectiveness of the tool in providing appropriate and efficient pedagogy and
assessment techniques; (c) the effectiveness of the tool in building the learning
objectives; (d) effectiveness of the tool in document generation; (e) Usability of the
tool; (f) the effectiveness of OBE on course design and expected student outcomes.
The thesis presents a detailed algorithm for course design feedback, its pseudocode, a
description and proof of the correctness of the feature, methods used for evaluation
of the tool, experiments for evaluation and analysis of the obtained results.
ContributorsRaj, Vaishnavi (Author) / Bansal, Srividya (Thesis advisor) / Bansal, Ajay (Committee member) / Mehlhase, Alexandra (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the inception of World Wide Web, the amount of data present on the internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. The aim of this thesis is to develop a Question Answering system to ease the process of information retrieval.
Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. Text REtrieval Conference (TREC) is a yearly conference which provides large - scale infrastructure and resources to support research in information retrieval domain. TREC has a question answering track since 1999 where the questions dataset contains a list of factual questions (Vorhees & Tice, 1999). DBpedia (Bizer et al., 2009) is a community driven effort to extract and structure the data present in Wikipedia.
The research objective of this thesis is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval and Natural Language processing. The focus is also on exploring the use of a structured and annotated knowledge base as opposed to an unstructured knowledge base. The knowledge base used here is DBpedia and the final system is evaluated on the TREC 2004 questions dataset.
Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. Text REtrieval Conference (TREC) is a yearly conference which provides large - scale infrastructure and resources to support research in information retrieval domain. TREC has a question answering track since 1999 where the questions dataset contains a list of factual questions (Vorhees & Tice, 1999). DBpedia (Bizer et al., 2009) is a community driven effort to extract and structure the data present in Wikipedia.
The research objective of this thesis is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval and Natural Language processing. The focus is also on exploring the use of a structured and annotated knowledge base as opposed to an unstructured knowledge base. The knowledge base used here is DBpedia and the final system is evaluated on the TREC 2004 questions dataset.
ContributorsChandurkar, Avani (Author) / Bansal, Ajay (Thesis advisor) / Bansal, Srividya (Committee member) / Lindquist, Timothy (Committee member) / Arizona State University (Publisher)
Created2016
Description
Availability of affordable image and video capturing devices as well as rapid development of social networking and content sharing websites has led to the creation of new type of content, Social Media. Any system serving the end user’s query search request should not only take the relevant images into consideration but they also need to be divergent for a well-rounded description of a query. As a result, the automated optimization of image retrieval results that are also divergent becomes exceedingly important.
The main focus of this thesis is to use visual description of a landmark by choosing the most diverse pictures that best describe all the details of the queried location from community-contributed datasets. For this, an end-to-end framework has been built, to retrieve relevant results that are also diverse. Different retrieval re-ranking and diversification strategies are evaluated to find a balance between relevance and diversification. Clustering techniques are employed to improve divergence. A unique fusion approach has been adopted to overcome the dilemma of selecting an appropriate clustering technique and the corresponding parameters, given a set of data to be investigated. Extensive experiments have been conducted on the Flickr Div150Cred dataset that has 30 different landmark locations. The results obtained are promising when evaluated on metrics for relevance and diversification.
The main focus of this thesis is to use visual description of a landmark by choosing the most diverse pictures that best describe all the details of the queried location from community-contributed datasets. For this, an end-to-end framework has been built, to retrieve relevant results that are also diverse. Different retrieval re-ranking and diversification strategies are evaluated to find a balance between relevance and diversification. Clustering techniques are employed to improve divergence. A unique fusion approach has been adopted to overcome the dilemma of selecting an appropriate clustering technique and the corresponding parameters, given a set of data to be investigated. Extensive experiments have been conducted on the Flickr Div150Cred dataset that has 30 different landmark locations. The results obtained are promising when evaluated on metrics for relevance and diversification.
ContributorsKalakota, Vaibhav Reddy (Author) / Bansal, Ajay (Thesis advisor) / Bansal, Srividya (Committee member) / Findler, Michael (Committee member) / Arizona State University (Publisher)
Created2020