Since the 1980s, CLT gave support to several instructional design models. CLT is based in uncontroversial aspects of human cognitive architecture. In the late 1990s, Sweller et al published extensive data using this theory that warranted an extended analysis (Sweller, van Merrienboer, Paas 1998). In 2019, the same authors (Sweller, van Merriënboer, Paas 2019), reviewed the theoretical and empirical work done so far, concluding that CLT, "(1) is firmly based in our—expanding—knowledge of human cognitive architecture; (2) it is under continuous development as our knowledge of human cognition advances; (3) it leads to testable hypotheses with possible negative results leading to modifications of the theory; (4) the vast bulk of the data generated by the theory is based on randomised, controlled trials; and (5) those randomised, controlled trials provide evidence for the effectiveness of instructional procedures that can be used in a wide range of educational contexts from conventional classrooms to e-learning, teaching all age groups from very young to adult learners, with an enormous range of subject matter from medical education to English literature."
The structures and functions of human cognitive architecture have been used to design a variety of novel instructional procedures based on the assumption that working memory load should be reduced and schema construction encouraged. One of this is 4C/ID instructional design which is extensively used in this work. Extensive research exists not only on the theories underlying the 4C/ID model but also in the inductive strategy applyed across several contexts (Khan et al., 2020), with results often summarized in specific effects and strategies, but in the effects itself resulting not only in the corroboration or not of those alredy in use but finding new ones that increase the effectiveness of the instruction. Consequently, stakeholders—including teachers, policymakers, and school administrators—can gain valuable insights to enhance teaching practices, improve learning design, and more effectively implement instructional strategies (Kirschner, 2002).
Direct Instruction (DI) model used in this work (Merrill, 2007), is a 200 years model with an extensive and profound report of positive impacts in learning. Developed and refined by Engelmann and colleagues over the past 50 years, has been the focus of numerous research studies, systematic reviews, and meta-analyses (Mason, Otero, 2021). Research on the effectiveness of Direct Instruction (DI) consistently provide support for the assertion that all children can learn in a wide variety of populations, in different settings, within numerous subject areas (Coughlin, Cristy, 2011). Furthermore, quantitative mixed models were used to examine literature published from 1966 through 2016 on the effectiveness of Direct Instruction. Analyses were based on 328 studies involving 413 study designs and almost 4,000 effects. Results are reported for the total set and subareas regarding reading, math, language, spelling, and multiple or other academic subjects; ability measures; affective outcomes; teacher and parent views; and single-subject designs. All of the estimated effects were positive and all were statistically significant except results from metaregressions involving affective outcomes (Stockard el al., 2018).
The 4C/ID model has been increasingly used in face-to-face and online learning environments. In a wide-ranging meta-analysis, several research programs concluded that this model should be promoted in both face-to-face and online learning environments (Sarfo, Elen, 2007), claiming that it has a high impact on performance, irrespective of the academic area, the study's design, or the specific outcome measured. Further analysis revealed that the higher-education level is particularly suitable for the application of the 4C/ID model (Costa, Miranda, Melo, 2022). The same authors showed results that revealed significant differences in learning transfer and a more marked decrease in the perceived mental effort by the students in the learning transfer tests (Melo, 2018).
Several reports show bLearning impact on student learning, confort, team work provided by the environment characteristics: flexibility, personalization, reflection proper time and groups discussion that foster higher-level thinking and independence (Collopy, Arnold, 2009).
In this work, the blended learning (bLearning) environment means using the LMS autonomously in class rather than at home. And also include some eLearning frameworks already widely tested (Garrison, Anderson 2003).
The available activities in the LMS were all guided. Students could revisit them, and this simple opportunity for repetition was a success. They were pleased that they could learn through trial and error, and in that way, they felt they were truly learning. Most students didn't use the LMS at home for two reasons: (1) few students had internet access, with rates only increasing from 25% to 50% between 2013 and 2017 1, and (2) the activities were designed to be completed in the school facilities, so there was no need to do them at home.
From the teacher's perspective, the autonomous nature of the students' work provided an opportunity to closely follow and assist with individual needs. This allowed the teacher to offer support on specific aspects of the activity and to help clarify a student's understanding of its overall goal.
The implementation and development of the MOODLE LMS was cumbersome and time-consuming, particularly when it came to content production and assessment revision. This task requires a team, as it is too demanding for a single professional, often leading to significant after-hours work and sleepless nights. This was also reported in other research authors (Collopy, Arnold, 2009). In some cases, students even performed revisions themselves while engaging with the LMS activities.
On the positive side, the fact that students were working autonomously gave the teacher the opportunity to monitor and address the specific needs of each individual.