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Evaluation of instructional module development system

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

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.

Contributors

Agent

Created

Date Created
2018

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Generating Vocabulary Sets for Implicit Language Learning using Masked Language Modeling

Description

Globalization is driving a rapid increase in motivation for learning new languages, with online and mobile language learning applications being an extremely popular method of doing so. Many language learning applications focus almost exclusively on aiding students in acquiring vocabulary,

Globalization is driving a rapid increase in motivation for learning new languages, with online and mobile language learning applications being an extremely popular method of doing so. Many language learning applications focus almost exclusively on aiding students in acquiring vocabulary, one of the most important elements in achieving fluency in a language. A well-balanced language curriculum must include both explicit vocabulary instruction and implicit vocabulary learning through interaction with authentic language materials. However, most language learning applications focus only on explicit instruction, providing little support for implicit learning. Students require support with implicit vocabulary learning because they need enough context to guess and acquire new words. Traditional techniques aim to teach students enough vocabulary to comprehend the text, thus enabling them to acquire new words. Despite the wide variety of support for vocabulary learning offered by learning applications today, few offer guidance on how to select an optimal vocabulary study set.

This thesis proposes a novel method of student modeling which uses pre-trained masked language models to model a student's reading comprehension abilities and detect words which are required for comprehension of a text. It explores the efficacy of using pre-trained masked language models to model human reading comprehension and presents a vocabulary study set generation pipeline using this method. This pipeline creates vocabulary study sets for explicit language learning that enable comprehension while still leaving some words to be acquired implicitly. Promising results show that masked language modeling can be used to model human comprehension and that the pipeline produces reasonably sized vocabulary study sets.

Contributors

Agent

Created

Date Created
2020