Research methodology

The research methodology for The SILO Project is Provisional Multimodal Research (PMR).  PMR is a research methodology designed for educators to document the construction of digital artefacts (Jacobs, 2024). In essence, PMR is simply archiving each version of digital artefacts while keeping a reflexive journal to document the rationale for any changes which are made. PMR "reflects a paradigmatic shift in scholarly communication—a move from monomodality to multimodality, from static representation to dynamic, embodied, and interactive meaning-making" (Akmalia & Faizin, 2026, p. 1). Furthermore, "by integrating digital devices and multimodal composition into pedagogy, educators can foster more inclusive, creative, and participatory forms of learning" (Akmalia & Faizin, 2026, p. 2). The main ideas involved in PMR can be summarised as follows:

The chronology of digital artefacts and the rationale for changes are mutually informative as shown in Figure 3.1.

Figure 3.1

Provisional Multimodal Research

Provisional Multimodal Research

The iterative nature of PMR means that the various pages of this website change frequently because incremental improvements are actioned on a daily basis. The SILO Project is built upon the following three premises:

  1. The international emphasis on STEM is likely to increase due to the rapid technological era in which we live.
  2. Time is scarce in primary schools. This has led to certain phrases such as the 'overcrowded curriculum'.
  3. The challenge for teachers is to help students develop and apply STEM knowledge in an efficient, equitable and authentic manner focusing on depth over breadth.


Data sources

The relationship between the data sources is shown in Figure 3.2.

Figure 3.2

Venn Diagram of the Data Sources


As noted in Jacobs (2024, p. 28):

A distinctive element of PMR is the role of provisional data. Instead of viewing provisional data as incomplete or as a placeholder for subsequent data, studies such as The SILO Project see all data as provisional because the learning is ongoing and there is always room for further improvement. Such improvements might be related to artefacts at the lesson level, unit level, or website level, all of which builds the knowledge base of the project as provisional data and reflection.

Co-construction

Another important methodological issue is co-construction and how teachers and researchers understand their own role as co-designers within the classroom. Much time and effort has gone into cultivating a learning environment based on mutual trust and respect to encourage the free flow of ideas in a spirit of collaboration. As yet, there have been no differences of opinion regarding implementation but the following three protocols are proposed to manage such instances:

  1. Ultimately, it is the classroom teacher who has the final say about what happens as it their classroom as they have a duty of care for everything which occurs. 
  2. If the researcher suggests an activity which is unfamiliar to the classroom teachers (such as coding micro:bits), the researcher will run the session so that the classroom teacher can observe without having to invest any additional preparation time
  3. If two or more classroom teachers within the same year level have a difference of opinion in relation to classroom activities, each teacher will remain free to pursue their chosen option. Such instances are likely to be generative as, "It is through understanding the recursive patterns of researchers’ framing questions, developing goals, implementing interventions, and analyzing resultant activity that knowledge is produced" (Barab & Squire, 2004, p. 10).

Teachers make countless decisions every day but the decision-making process which guides such decisions is rarely articulated because it is tacit knowledge. Figure 3.3 seeks to make this tacit knowledge visible in the context of STEM education.

Figure 3.3

A Decision-Making Tool for STEM Education


Figure 3.3 is largely based on common sense and professional judgement but a simple tool like this brings some larger issues into focus such as relevance and suitability. It also shows teachers where they might need to expand their skills, knowledge or resources. 

Assessment

As teachers, we are familiar with the various types of assessment shown in Figure 3.4.

Figure 3.4

An Overview of Assessment Types



At the bottom of each of The 28 STEM units is the following rubric as shown in Figure 3.5.

Figure 3.5

A Rubric for Moderating Self-Assessments

As noted by Jacobs and Cripps Clark (2018), progress through this rubric occurs from top to bottom. The implications for this phenomenon are as follows:
  1. Initial research for a conceptual topic begins by first identifying, and then using, correct terminology.
  2. An eventual outcome of investigating correct terminology is the identification of relevant components.
  3. The pinnacle of conceptual consolidation involves understanding the dynamic relationships that exist between the different components.
  4. Conceptual consolidation itself must be understood on a case-by-case basis because, regardless of any similarities, every concept is different (Jacobs & Cripps Clark, 2018, p. 47).

Figure 3.6 is static composite of the animated GIF in Figure 3.5 so you can see the progression without being distracted by the movement.

Figure 3.6

Static Composite of the Moderated Self-Assessment Rubric


The following paragraph provides an example of how the rubric can be used to benchmark student achievement for a particular topic. It is taken from SILO 2.3 'Fair tests'.

A student: (1) defines a variable; (2) outlines the requirements of a fair test; (3) can discuss the requirements of a fair test from memory in any order; (4) can explain why the independent variable is the focus of an experiment; (5) can measure the dependent variable and relate this measurement to the independent variable; (6) can explain why an experiment is only a fair test if the control variable(s) can be kept constant; (7) designs their own fair test; (8) writes instructions for their fair test with clearly defined variables; (9) formulates a hypothesis for their fair test; (10) can explain the relationship between fair tests and hypotheses.




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