This is a practical commentary paper rather than an empirical study. Its argument is straightforward and deliberately conciliatory: instead of continuing the long-running debate about whether inquiry-based instruction or direct instruction is superior, the authors ask what happens when you apply the design principles of direct instruction inside an inquiry-based learning environment. The paper works through a set of direct instruction principles one by one and shows, using concrete examples from the Go-Lab digital science learning platform, how each can be embedded within inquiry without undermining what makes inquiry valuable.
The authors begin by defining their terms carefully. Inquiry-based instruction, in their framing, requires that students generate, infer or construct essential knowledge themselves through activities like hypothesis formation, experimentation and data analysis — knowledge is not simply given to them. Direct instruction, by contrast, presents all knowledge explicitly, models skills before students practise them, and structures learning to prevent errors. The one direct instruction principle they explicitly exclude from their synthesis is total teacher modelling of content, because that is fundamentally incompatible with inquiry. Every other principle, they argue, translates.
The principles they work through are as follows. Presenting learning objectives at the outset — standard practice in direct instruction — is fully compatible with inquiry because telling students what concepts they should understand by the end does not constrain the route they take to get there. Activating prior and prerequisite knowledge is treated as a shared requirement of both methods, not a distinctively direct instruction feature; the Go-Lab examples show this done through diagnostic quizzes and semi-structured concept maps. Generating wonderment — capturing students’ attention through surprising phenomena, counterintuitive predictions or narrative challenges — is positioned as an inquiry-friendly analogue to the “gaining attention” step in direct instruction lesson design. Structuring the learning sequence, or “thin-slicing” the content into manageable steps that build progressively in complexity, is shown to work within inquiry through sequenced investigations that guide students through variables one at a time. Guided practice, in the inquiry context, cannot involve telling students the right answer — instead it means giving targeted experimental assignments designed to address specific misconceptions, with feedback systems built into the platform. Reflection — a feature of both traditions but more explicitly foregrounded in direct instruction — can be scaffolded through structured prompts at the conclusion and discussion phases of an inquiry cycle. Metacognitive scaffolding, supporting students to monitor and regulate their own inquiry process, is the final principle addressed, illustrated through tools that show students a visual map of their progress through an investigation relative to their peers.
Throughout, the Go-Lab ecosystem serves as the working demonstration environment. Go-Lab is an online platform widely used in European science education that allows teachers to design “Inquiry Learning Spaces” built around digital laboratories, with configurable apps for each phase of an inquiry cycle. The examples span mathematics, biology, chemistry and physics, and are specific enough to be practically useful rather than merely illustrative.
The paper concludes by reaffirming its central position: the debate between inquiry and direct instruction has been framed as a binary choice when it need not be. Most of the structural and pedagogical wisdom accumulated in direct instruction research — clear goals, sequenced content, activated prior knowledge, guided practice with feedback, metacognitive support — is available to inquiry-based designers without requiring them to abandon the core principle that students must construct knowledge themselves.