A variety of common markup showing how the theme styles them.
Header one
Header two
Header three
Header four
Header five
Header six
Blockquotes
Single line blockquote:
Stay hungry. Stay foolish.
Multi line blockquote with a cite reference:
People think focus means saying yes to the thing you’ve got to focus on. But that’s not what it means at all. It means saying no to the hundred other good ideas that there are. You have to pick carefully. I’m actually as proud of the things we haven’t done as the things I have done. Innovation is saying no to 1,000 things.
Steve Jobs — Apple Worldwide Developers’ Conference, 1997
The abbreviation CSS stands for “Cascading Style Sheets”.
Cite Tag
“Code is poetry.” —Automattic
Code Tag
You will learn later on in these tests that word-wrap: break-word; will be your best friend.
Strike Tag
This tag will let you strikeout text.
Emphasize Tag
The emphasize tag should italicize text.
Insert Tag
This tag should denote inserted text.
Keyboard Tag
This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.
Preformatted Tag
This tag styles large blocks of code.
.post-title {
margin: 0 0 5px;
font-weight: bold;
font-size: 38px;
line-height: 1.2;
and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}
Motivated by the importance of education in an individual’s and a society’s development, researchers have been exploring the use of Artificial Intelligence (AI) in the domain and have come up with myriad potential applications. This paper pays particular attention to this issue by highlighting the future scope and market opportunities for AI in education, the existing tools and applications deployed in several applications of AI in education, research trends, current limitations and pitfalls of AI in education. In particular, the paper reviews the various applications of AI in education including student grading and evaluations, students’ retention and drop out prediction, sentiment analysis, intelligent tutoring, classrooms’ monitoring and recommendation systems. The paper also provides a detailed bibliometric analysis to highlight the research trends in the domain over six years (2014–2019). For this study, we analyze research publications in various related sub-domains such as learning analytics, educational data mining (EDM), and big data in education. The paper analyzes educational applications from different perspectives. On the one hand, it provides a detailed description of the tools and platforms developed as the outcome of the research work achieved in these applications. On the other side, it identifies the potential challenges, current limitations and hints for further improvement. We also provide important insights into the use and pitfalls of AI in education. We believe such rigorous analysis will provide a baseline for future research in the domain. Read more
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