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.
This has lead to NLP pipeline architectures that integrate components that solve a specific aspect of the problem and pass on the results to subsequent components for further processing eg: DBpedia Spotlight for named entity recognition, RelMatch for relational mapping, etc. A major drawback in this approach is error propagation that is a common problem in NLP. This can occur due to mistakes early on in the pipeline that can adversely affect successive steps further down the pipeline. Another approach is to use query templates either manually generated or extracted from existing benchmark datasets such as Question Answering over Linked Data (QALD) to generate the SPARQL queries that is basically a set of predefined queries with various slots that need to be filled. This approach potentially shifts the question answering problem into a classification task where the system needs to match the input question to the appropriate template (class label).
This thesis proposes a neural network approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination for the need of laborious feature engineering that can be cumbersome and error prone. The input question would be encoded into a vector representation. The model will be trained and evaluated on the LC-QuAD Dataset (Large-scale Complex Question Answering Dataset). The dataset was created explicitly for machine learning based QA approaches for learning complex SPARQL queries. The dataset consists of 5000 questions along with their corresponding SPARQL queries over the DBpedia dataset spanning 5042 entities and 615 predicates. These queries were annotated based on 38 unique templates that the model will attempt to classify. The resulting model will be evaluated against both the LC-QuAD dataset and the Question Answering Over Linked Data (QALD-7) dataset.
The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset.
After slot filling, the overall system achieves a macro F-score 0.419 on the LC- QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset.
Class instructors at Arizona State University monitor students’ attendance for classes in which attendance is either mandatory or encouraged. Class monitoring can be done using traditional systems such as sign sheets and roll calls. From my initial observations while attending a class which utilized a sign sheet for class attendance monitoring, I thought the process took long and was inefficient. As a result, I created an automated system that would replace the traditional systems and improve the class monitoring process. Thus, this study aims to determine whether the automated system reduced the time it takes to monitor class attendance, and whether it was efficient.
To examine the above question, the automated system was deployed to 2 classes at Arizona State University. Additionally, surveys were distributed to 2 instructors and 33 students and they were asked to respond to questions relating to class attendance and the monitoring systems which were being used alternatively with the newly-created automated system. Analysis of the responses demonstrated that use of an automated system reduced the time it takes students to mark their presence, and thus increase the time used for other class activities. The results also indicate that the design of the automated system affects the overall time it takes to monitor attendance. On this basis, it is recommended that instructors utilize an automated system to monitor class attendance. Further research is needed to study the time it takes instructors to set up different monitoring systems in order to ascertain that an automated system reduces the overall time it takes to monitor attendance compared to other traditionally used systems.
Compass portal features tools that help teachers, psychologists, behavioral specialists gain insights on students’ performance through activities they have completed.
COMPASS portal features tools that help teachers, psychologists, behavioral Specialists gain insights on students’ performance through activities they have completed.
This thesis proposes a new steering system for agricultural machinery with the aim of improving the automation capabilities of farming robots. Accurate and reliable autonomous machinery has the potential to provide significant benefits to the efficiency of farming operations, but the existing systems for performing one of the most essential automation functions, autonomous steering to keep machinery on the proper course, each have drawbacks that impact their usability in various scenarios. In order to address these issues, a new lidar-based system was developed for automatic steering in a typical farm field. This approach uses a two-dimensional lidar unit to scan the ground in front of the robot to detect and steer based on farm tracks, a common feature in many farm fields. This system was implemented and evaluated, with results demonstrating that the system is capable of providing accurate steering corrections.