The MELTS Research Agenda

New! See our presentation from the International TESOL Conference 2019
Elementary teacher candidates need better preparation for teaching English learners (ELs).

Micro-credentialing recognizes the completion of formal and informal learning and can be earned through the demonstration of a particular micro-skill or practice (Hurst, 2015).

Devedzic and Jovanovic (2015) found several positive aspects of incorporating digital badges into curricula. Digital badges support goal setting, planning, and self-reflection, recognize under- or non-recognized skills as well as prior learning, and develop a sense of community membership.

Multi-user virtual environments (MUVEs) are computer-simulated environments that foster interaction between real-life participants and online personas or avatars (Dede, Clarke, Ketelhut, Nelson, and Bowman, 2005).

MUVEs “allow individuals to have repeated trials involving high stakes situations without risking the loss of valuable resources” (Dieker, Rodriguez, Lingugaris, Kraft, Hynes, and Hughes, 2014, p. 22).

MUVEs can be a powerful teaching and learning environment for achieving instructional objectives (Dede, Clarke, Ketelhut, Nelson, and Bowman, 2005).

Research Questions—Mixed Methods

  1. How is teacher candidates’ ultimate acquisition of 8 EL instructional micro-skills affected by their participation in simulation or microteaching performance tasks?
  2. What is the difference in English learners’ gains on classroom-based unit tests based on their intern’s micro-skills preparation mode: simulation (experimental group A), microteaching (experimental group B), or neither (control group).
  3. How do interns apply the micro-skills they acquired in their classrooms, if at all?
  4. Research Design—Question 1, Experimental Groups A & B

Using a quasi-experimental design, teacher candidates will be assigned to one of two sequences of junior and senior-level teacher preparation courses, those that use interaction with EL avatars (Simulation Group—Group A) and those that use microteaching with peers (Microteaching Group—Group B).

Both groups will watch demonstration videos, complete an instructional module about the micro-skills and its use with English learners at different levels of proficiency, participate in service learning experiences with ELs as well as simulation activities that do not include ELs.

Data Collection & Analysis—Question 1, Experimental Groups A & B

For both experimental groups, performance assessments will be conducted in the simulated classroom including EL avatars during semesters 1 and 3 of the curricular sequence [include during & post coaching for all candidates]. A sample of the interactions will be video-recorded and closely transcribed for discourse analysis and will be coded for micro-skills demonstration with an online tool for video coding (ReflectLive). All participants will submit written reflections on their developing competency in teaching English learners at the midpoint and final assessments. Data Collection & Analysis—Question 2, Experimental and Control Groups

The semester after the 4-semester sequence, participants from the simulation and microteaching groups will complete their final internship in a classroom with one or more English learners. Each intern will complete a Teacher Work Sample during internship, which will include performance data for all students in the intern’s classroom. English learners’ classroom-based assessment data for the simulation and microteaching groups (MELTS Cohorts n=300) and for a group of UCF pre-service teachers that did not participate in the simulation or microteaching activity sequence (n=300) will be analyzed using Propensity Score Matching (PSM) to determine if their ELs’ gains are comparable.

Data Collection & Analysis—Question 2, Propensity Score Matching (PSM)

In study designs in which participants are not (or cannot be) randomly assigned to the treatment or control group, it is difficult to know with confidence that any observed differences between groups were caused by the treatment rather than by existing differences between each group. PSM methods use any and all data available about the participants (such as demographic data, prior achievement, or anything else relevant to the study) to calculate the probability of each participant being in one group or another. These scores are then matched across groups to yield a directly comparable subset of participants which are equivalent prior to the treatment, thus allowing causal conclusions.

Data Collection & Analysis—Question 3, Experimental Groups A & B

The researchers will observe a representational sample of teacher candidates who completed both sequences using the SIOP and a grant-developed observation form to determine if the micro-skills are being applied in the classroom setting during internship (for all 3 cohorts) and the first year of teaching (for Cohort 1).