research project -- the Praxis Series: Praxis I. As-
sessment of Students Skills; Praxis II. Assessment of
Subject Matter Literacy; and Praxis III. Assessment
of Classroom Performance. The significance of the
first two is to award primary teaching qualifications
to those who pass them, while the latter revolves
around the assessment of teachers’ actual teaching
skills and classroom performance (Zhou 2017). Pro-
fessor Danielson and her team were responsible for
the development of Praxis III. However, during the
study, Danielson was adamant that Praxis III was not
only a tool to assess classroom competence, but also
to improve teachers’ professional development.
Subsequently, based on Praxis III, Danielson and her
team completed Danielson Framework for Teaching
after continuous refinement and extended it to sup-
port teachers’ professional development in teaching
practices in states across the United States (Char-
lotte, Danielson 2013).
2.2 Background of the Marzano
Teacher Evaluation Model Based
on Artificial Intelligence
The Marzano Teacher Evaluation Model (MTEM)
was developed for three general reasons. First, since
the 1980s, neoliberalism has prevailed, and the edu-
cational process has become more concerned with
cost-cutting, standard-setting, and educational output
(Lu 2006). Educational outputs are reflected in the
increased focus on students’ test scores and have
eventually been applied to teacher evaluation (Hursh
2005). Secondly, the Council for the Accreditation
of Educator Preparation (CAEP) proposed in late
2012 that standards for educational evaluation sys-
tems should provide multiple assessment indicators.
In response to the need for diversified teacher evalu-
ation, Marzano and his team created a new teacher
evaluation model by distilling and summarizing the
core competencies of teachers through scientific
evidence, which provided multiple options for the
development of teacher professionalization.
Thirdly, Marzano has always been passionate
about research on classroom practice, teacher evalu-
ation, and school leader assessment, and has been
committed to effectively applying the latest theories
to classroom practice (Larsen 2015). The Marzano
Teacher Evaluation Model was developed by Mar-
zano and his team based on years of research, with
key findings such as, What Works in Schools, Class-
room Instruction that Works, Classroom Manage-
ment that Works, Classroom Assessment and Grad-
ing the Work, The Art and Science of Teaching,
Effective Supervision: Supporting the Art and Sci-
ence of Teaching (Marzano Center, 2015).
In short, the emergence of the Marzano Model is
closely linked to the deepening of neoliberalism and
accountability in public education in the United
States, the aspirations of the American Council for
the Accreditation of Teacher Education for plural-
istic teacher evaluation, and Marzano’s tireless ef-
forts.
2.3 Artificial Intelligence Application
Areas
2.3.1 Natural Language Processing
Natural language processing consists of two parts:
natural language understanding and natural language
generation. The function of natural language under-
standing technology is to enable computers to under-
stand the meaning of natural language text, and natu-
ral language generation technology is to enable
computers to express given ideas and intentions in
natural language text. The purpose of developing
natural language processing technology is to prevent
people from spending a lot of time and effort to
learn various obscure computer languages, and to
allow people to use the natural language they are
most familiar with and accustomed to in order to
achieve natural language communication between
humans and computers.
2.3.2 Big Data Analytics
Big data analytics is the ability to process data of all
types and shapes and to capture the information
value of massive and high-growth data in a new
processing model. By collecting, storing and mining
data, big data analytics can help human beings find
the correlation between known variables and make
scientific and intelligent decisions accordingly.
There is a large amount of data in the process of
education and teaching, and the targeted construc-
tion of AI analysis models can help teachers identify
the shortcomings in teaching and provide improve-
ment solutions by analyzing these data with the help
of big data analysis technology. The level of applica-
tion of artificial intelligence in education and teach-
ing depends on the upgrading and improvement of
big data analysis technology.