Analyzing the Novel Approaches for Intelligent Computer-Aided Learning (ICAL)

Analyzing the Novel Approaches for Intelligent Computer-Aided Learning (ICAL)

Shaheen H.
DOI: 10.4018/978-1-6684-5058-1.ch007
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Abstract

Beyond the spread of the disease and efforts to treat, the coronavirus pandemic has far-reaching implications. It is crystal clear that the pandemic wreaked havoc on critical fields such as healthcare, business, and academic. In terms of academics, one direct outcome of the pandemic's spread was the decision to suspend traditional in-person classroom courses in favor of remote learning and home-based schooling through the use of computer assisted learning technologies, but these technologies confront numerous hurdles. The majority of these issues revolve around the effectiveness of these delivery modalities, as well as engagement and knowledge testing. These difficulties highlight the need for an advanced smart educational system that aids all types of learners, gives teachers a variety of smart new tools, and enables a more flexible learning environment. The internet of things, artificial intelligence and virtual reality is a blooming sector, and IoT-based devices have ushered in a revolution in electronics and information technology.
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Introduction

The concept of Computer-Assisted Learning (CAL) was introduced in 1960 itself in the PLATO Project, initiated by Illinois University. It is developed in each decade till now. CAL's present theory emphasizes student-centered skills that enable students to work independently (Wikipedia contributors, 2022). As the name suggests, computer assisted learning (CAL) is the use of electronic devices/computer devices to deliver educational instructions to educate people. Computer-Assisted Learning can be applied in almost any field of education, from kindergarten children's Television based game programs earlier and computer-based programs nowadays also medical people studying quad bypasses treatment methods. Knowledge from all domains of education/learning, Human-Computer Interaction (HCI), and cognitive has been combined to create CAL (Wang and Wu, 2020).

These resources might be structured or unstructured, but they usually include two key elements: participatory training and personalized learning. CAL is essentially a technology that aids teachers in making the language learning process easier for students. It can be used to reinforce what students have also already learnt in class or as a remediation tool for students who need more help. CAL products are made with language education and method in mind, which can be taken from a range of instructional concepts (e.g., cognitive, behaviorist, constructivist) and second-language development theory. The definition of blended learning is the combo of CAL and face to face learning (Gunawardhana, 2020).

Typology

There are many CAL methods identified once it starts to bloom. The Scientists Warschauer and Healy (1998) outlined there are three phases to determine the CAL types from the 1960s as shown in the figure 1. In Type-1 approach Students' inputs will be analyzed and feedback will be provided, and more advanced algorithms would react to errors by diverging to assistance screens as well as corrective tasks. Since such programmes and their underlying methodology still exist today, most language teachers reject behavioristic effects on language acquisition, and the rising power of new technologies has driven CAL to explore other options. The Next type is communicative strategy The emphasis is on communication rather than analysis, and grammar is taught implicitly rather than formally. It also permits students' linguistic outputs to be more creative and flexible. The communicative approach coincided with the introduction of the personal computer, which made computers considerably more accessible and resulted in a surge in the creation of linguistic programs. In the final type attempts were made to address concerns of the communicative approach by incorporating language ability instruction into tasks and assignments to offer direction and coherence. It also happened to be about the same time as the invention of multimedia technology (textual, images, audio, and animations) and computer-mediated interaction.

Figure 1.
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Challenges And Bottlenecks Of Computer Assisted Learning

Learners could be unfamiliar to what will be provided to them the first-time computers are brought into the educational process, resulting in general nervousness. If the learners (especially the older ones) are still not computer proficient, the educator will have to deal with technophobia as well. In this setting, it would take a long time for pupils to become accustomed to CAL.

Learners may become overwhelmed by the amount of data they are processing in a computer-based self-assessment class where they are left on their own. As a result, excessive use of multimedia relays should be avoided while teaching CAL for the first attempt, as well as the instructor should concentrate further on the topic getting taught because learners' gaze is very often drawn to the computer.

Key Terms in this Chapter

ICAL: Artificial Intelligent Computer Assisted Learning

IoT: Internet of Things

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