Data from large-scale assessments must be used with caution when making judgments about individual student performance because of the narrow constraints of the types of knowledge and skills that can be assessed through such a format.
Procedural fluency is a critical component mathematical proficiency and is more than memorizing facts and procedures.
Practices
that support access and equity require comprehensive understanding and require
being responsive to students’ backgrounds, experiences, cultural
perspectives, traditions, and knowledge.
Young learners’ future understanding of mathematics requires
an early foundation based on a high-quality, challenging, and accessible
mathematics education.
Mentorship
is important in shaping and developing the next generation of teachers,
particularly as expectations for students become more rigorous.
Computer science should be incorporated into the curriculum in a way that enhances, rather than limits, students’ college and career readiness in mathematics.
For too long, our educational systems have privileged some students in the mathematics classroom while marginalizing others.
Professional
development courses and workshops for future and current teachers need to model
effective pedagogies for teaching statistics, in addition to focusing on
developing understanding of statistical concepts, mastery of statistical
content, and knowledge of the essential ideas of statistical thinking and
problem solving. (A
joint position statement of the American Statistical Association and the
National Council of Teachers of Mathematics.)
The
ultimate goal of the K–12 mathematics curriculum should not be to get students
into and through a course in calculus by twelfth grade but to have established
the mathematical foundation that will enable students to pursue whatever course
of study interests them when they get to college. (A joint
position statement of the Mathematical Association of America and the National
Council of Teachers of Mathematics.)
To teach mathematics with high expectations means that teachers recognize that each and every student, from prekindergarten through college, is able to solve challenging mathematical tasks.
This updated joint position of the Association of Mathematics Teacher Educators (AMTE), the Association of State Supervisors of Mathematics (ASSM), NCSM: Leadership in Mathematics Education (NCSM) and the National Council of Teachers of Mathematics (NCTM) calls for elementary mathematics specialists to help ensure equitable and effective mathematics learning for each and every student.
Professional growth and support should be the foremost goals of any teacher evaluation process, which should be led by those knowledgeable about effective mathematics instruction.
Technology is a catalyst for change and innovation in our global society. Technology advancements allow for building mathematical models as well as exploring large data sets in ways that seemed inconceivable even a decade ago. Such advances in technology must be reflected in mathematics programs and class- rooms in ways that are thoughtful and keep the learning of mathematics at the forefront of students’ experiences.
Do you, your school, or your district attempt to ensure that all students have access to mathematics learning opportunities that are rigorous, challenging, and affirming of their identities as learners and human beings? At the same time, do you, your school, or your district use terms such as “high,” “advanced,” “gifted,” “below basic,” “far below basic,” “remedial,” and “low” to characterize students’ mathematical abilities? These practices are fundamentally incompatible. If our society is to ever make high-quality mathematics teaching and learning for each and every student a reality, the practice of ability labeling must end.
Ensuring that all students have the mathematical experiences necessary to increase their opportunities for personal and professional success is essential. Data science is a rigorous, engaging, and practical field of study and can be a significant part of a high school student’s mathematical experience. Knowledge of data science is important, and a data science course should be accepted as a high school mathematics course that can be used for credit towards graduation, provided the course includes or builds on previous, substantive student work with essential concepts, knowledge, skills, and habits of mind in mathematics and statistics, as described in Catalyzing Change.
A coherent, well-articulated curriculum is an essential tool for guiding teacher collaboration, goal-setting, analysis of student thinking, and implementation.
Artificial Intelligence (AI)-driven tools can respond to students’ thinking and interests in ways that previous tools could not. By drawing from large language sets, AI has the potential to adjust application-based problems to student interests and identify the sense students have made even in their incorrect answers. Students will continue to need teachers’ mathematical, pedagogical, and relational expertise, though teachers are also likely to benefit from AI-driven tools.
Students
need a strong mathematics foundation to succeed in STEM fields and to make
sense of STEM-related topics in their daily lives.
Linking research and practice in mathematics education is necessary to address critical issues of mathematics teaching and learning.