Abstract:

Lectures are tools for driving the learning process. Researchers observed a direct relation between lecturing styles and learning strategy adopted and proposed models for designing excellent quality lectures. In their models, they consider lecture entities namely introduction, body and conclusion as a single unit. These entities are complicated, so they must be designed separately. This research focuses on the fabrication of a software application named "LECON-APP" which helps in developing excellent quality lecture-conclusion (LECON).

This research proposes the reference architecture of the desktop software application named "LECON-APP". This application automates the LECON design process and helps in producing good quality LECON's. This application uses the "LEC-CONCD" model for designing LECON's (*note a separate paper dedicated to LEC-CONCD model entitled "LEC-CONCD: A MODEL FOR DESIGNING GOOD QUALITY LECTURE CONCLUSIONS" is submitted to ICERI-2020).

The "LEC-CONCD" model uses pedagogical, linguistic and computer engineering related concepts like:
(a) content complexity rigor (CCR),
(b) depth of knowledge (DOK),
(c) knowledge points (KP),
(d) learning styles,
(e) controlled natural languages (CNL),
(f) syntax parsing and (g) others to design quality LECON's.

The "LEC-CONCD" model classifies LECON's under four categories, namely:
(i) simple summary,
(ii) single-KP conclusions,
(iii) multi-KP conclusions and
(iv) visual graphs.

Our software application is capable of designing LECON's based on classification categories discussed above.

To check the usability and quality of the designed application, we conducted a software testing program with 60 naive participants. The duration of the testing program was of two hours. Initially, the participants were trained (i.e. given demo) for the first fifteen minutes on "how to use the application". Then for the next 1 hour 30 minute, they designed LECON's using the app, and for the last 15 minutes, they rated the software and filled the feedback form. Based on their ratings and feedbacks, we observed many interesting key points which helps us to improve our application in the next update iteration. One such major point is regarding improvement in interactiveness of the user interface, where few users asked to change the interactivity using graphical user interface (GUI) components and overall look and feel. In another suggestion, few participants asked to attach a new palette for better designing of text-based input.

After an extensive literature survey, we selected "overall performance", "learnability" and "simplicity" as quality attributes (QA) for checking the quality of our app. The participants rated our app concerning these QA's using the 5-point scale {"very-bad", "bad", "average", "good", "excellent"}. We found several tradeoffs among selected QA's, few of them are as follows:
(a) Out of 60 participants, {31, 24} participants rated our application as {excellent, good} concerning "overall performance" QA, respectively.
(b) {55, 52} candidates rated our app as either excellent or good concerning "learnability", "simplicity" QA's respectively.
(c) others.

We believe that the development of such applications helps the education industry and online educators to perform their tasks effectively.

Keywords:

Knowledge points, depth of knowledge, reference architecture, controlled natural languages.