Blended Learning Immersion Teacher Education: Evidence-Based Practices and Data-Driven Instruction

The practice of blending different learning approaches and strategies in higher-level education is not new, yet our understanding of how to design the most effective and efficient blend remains incomplete. Challenges are further compounded when students are not fully proficient in the language of instruction. However, teacher educators learn about teaching through learning about student learning. Evidence-based practices and data-driven instruction create conditions for success in blended learning design and implementation. This chapter reviews the impact of a blended learning professional development (PD) initiative, with a dual focus on language and content, on Irish-medium immersion (IMI) teacher development. Findings provide unique insights in relation to the effectiveness of a blended learning PD experience as indicated by student motivation, autonomy and success. Linguistic and pedagogical capacity were fostered and community cultivated. Lessons learned and tutor reflections are also shared in an attempt to advance learning in the field and to cultivate future innovation in policy, practice and possibilities.

Personalizing Learning Targets With Technology-Based Assessment

There is tension between standardization and individualization in education today. Instructional fidelity to a challenging grade-level curriculum is the expectation of current pedagogy. Federal U.S. initiatives mandate assessing the academic growth of all students with common assessments aligned with challenging content standards. The growing number of students who vary as learners in today's classrooms holds implication for instruction and assessment. Personalizing learning targets promotes an equitable measure of student growth. Appropriate instructional goals develop grade-level curricular skills that are selected based on student performance data and are personalized with individualized baselines and proficiency targets. Technology facilitates data-driven instruction through the efficient development and progress monitoring of personalized learning goals. In this chapter, a technology-based model for personalizing standards-based learning targets, developing SMART goals, and monitoring progress is presented.

Using Culturally Responsive Teaching and Data-Driven Instruction in Mathematics to Create Equity and Equality

The diverse nature of school populations in current education requires teaching that not only considers the differences in their students, but how their own practices have an impact on a student's ability to learn content. Traditional Eurocentric models of education are not relevant to the lives of racially culturally ethnically linguistically diverse students, thus creating barriers to learning for students who do not fit neatly into categories of Black or White. For the academic environment to be accessible for all students, classroom instruction must become culturally aligned. This may be somewhat of a challenge given that classroom teachers are ill-prepared to meet the needs of RCELD students in math education. Teachers are faced with the critical challenge of creating learning environments that bridge the gap between culture and curriculum with appropriate strategies and resources, while all the while adhering to state and federal standards. This chapter explores using culturally responsive teaching to teach mathematics.

L2 Chinese learners’ pragmatic developmental patterns in data-driven instruction and computer-mediated communication (CMC)

This study examined second language (L2) Chinese learners’ developmental patterns of pragmatic competence in two computer-mediated communication (CMC) conditions: (1) CMC with data-driven instruction embedded in the course of CMC and (2) CMC without data-driven instruction. Learners’ pragmatic competence was operationalized as their ability to use a Chinese sentence final particle (SFP) ne during CMC with a native speaker partner. The study investigated: (1) whether learners (as a group) developed their use of ne over time in the two CMC conditions, and (2) how individual learners changed their use of ne (if any) in the two conditions. The quantitative analysis (token and type frequency of ne) revealed that CMC itself did not promote learners’ use of ne. However, it promoted learners’ production of ne when data-driven instruction was incorporated into CMC. Supporting the quantitative findings, the qualitative analysis showed that one learner in the CMC with data-driven instruction outperformed his counterpart in the CMC without data-driven instruction group in the diverse use of ne.

Data Do Not Drive Themselves

Several years ago, I was working with a group of high school math teachers. Their assistant principal was impressed with their practice of sharing data from common assessments, assuming that they used these data to drive instruction. However, when I asked the teachers which data they used when teaching, they said that student work and questions during class were much more valuable. Apparently, people may interpret “data-driven instruction” differently. As a mathematics teacher, what data can you collect, and how can you use those data to improve instruction?

Using Culturally Responsive Teaching and Data-Driven Instruction in Mathematics to Create Equity and Equality

The diverse nature of school populations in current education requires teaching that not only considers the differences in their students, but how their own practices have an impact on a student's ability to learn content. Traditional Eurocentric models of education are not relevant to the lives of racially culturally ethnically linguistically diverse students, thus creating barriers to learning for students who do not fit neatly into categories of Black or White. For the academic environment to be accessible for all students, classroom instruction must become culturally aligned. This may be somewhat of a challenge given that classroom teachers are ill-prepared to meet the needs of RCELD students in math education. Teachers are faced with the critical challenge of creating learning environments that bridge the gap between culture and curriculum with appropriate strategies and resources, while all the while adhering to state and federal standards. This chapter explores using culturally responsive teaching to teach mathematics.