In this
digital and highly technical world, people are more dependent on
automation, software technology, app, and mechanics than on manual
activities. Therefore with the advancements in the research sector,
the researchers have brought a vital connection between mechanical
science and psychological science. Specific software and app have
already been introduced in this technical world following the
footprints of the Artificial Intelligence concept that can sense the
presence of a human being within a specific range of areas. Thus
accordingly, there has also been an improvement in the field of AI
that enables a machine to perform all human-related activities like
driving, cleaning, cooking, reading, etc.
The
advent of the AI-enabled Software for Identifying Students’ Need
According to
the reports of the recent researches, it has been found that an
artificial
intelligence model can predict the quality of learning of the
students in the educational games. The improved model of the
application software has used an AI training concept called as
multi-task
learning. Thus this is used to improve both the instructions as
well as the learning outcomes. In this approach of multi-task
learning, one model is asked by the user to perform multiple tasks.
In the case of researching the educational
game, all the researchers wanted the model to predict whether a
student can answer each question correctly on a conducted test. This
action is said to be predicted based on the behavior of the student
while playing an educational game called Crystal
Island. Thus viewing the test as one task, the standard approach
for solving the problem is looked for only at the overall test score.
Multi-Tasking
Learning Model for Students
In the
context of the researcher, the model of the multi-tasking learning
framework has 17 tasks, as the test has 17 questions. The team of
researchers had the gameplay along with the testing data from 181
students. The concept of AI applied looked at the gameplay of each
student and even the way each student answered Question 1 on the
test. With the identification of the common behaviors of the students
that answered say Question 1 correctly and the identification of the
students who got Question 1 wrong, the AI could determine that how a
new student would answer Question 1. Therefore, at the same time,
this function is performed for every question, and the gameplay that
is being reviewed for a given student is the same. But accordingly,
AI keeps on looking at the behavioral pattern in the context of
Question 2, Question 3, and so on.
Hence, this
AI-enabled multi-task approach made a difference. In relevance, the
researchers found that the multi-task model was about 10 percent more
accurate than the other types of models that relied on the
conventional methods of AI training. This model is designed so to be
used in a couple of ways that can benefit the students. This kind of
model can be used to notify the teachers when the gameplay of a
student suggests that the student might need additional instruction.
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