Cultivating Fieldwork Skills Through a “Choose Your Own Adventure” Interactive Virtual Field Trip
Bibliographic record
Abstract
Fieldwork is an essential part of geoscience training. It teaches spatial reasoning, teamwork, and organizational skills while requiring integration of diverse observations and iterative hypothesis testing. Successful field campaigns demand critical thinking, problem-solving, and adaptability—skills that many students find challenging, particularly when faced with open-ended tasks that lack “correct answers.” Moreover, physical fieldwork can also be exclusionary, particularly for students with health or mental impairments, lower socioeconomic status, or inflexible family obligations that limit their ability to enter the field. To address these barriers, we need inclusive and innovative methods to teach transferable field skills to all students, regardless of their ability to participate in physical fieldwork. Interactive Virtual Field Trips (iVFTs) offer a promising solution by enabling students to explore spatially integrated, data-rich environments and “visit” inaccessible sites at their own pace with fewer external stressors.We present an iVFT to the Mont Albert ophiolite complex (Québec, Canada), designed to train and assess students in field preparation and critical thinking in an accessible, inclusive setting. We built the iVFT as a “choose your own adventure", challenge-based virtual environment that provides a structured yet flexible framework for cultivating field skills such as strategic planning, data integration, and decision-making in dynamic scenarios. The environment integrates desktop virtual reality with an option of VR/AR compatible glasses for full immersion. To prepare for the 'field' activity, we instruct students to plan an initial field campaign justified by their chosen research problem and terrane accessibility inferred from a topographic map, and "pack a backpack" based on logistical constraints (including weight estimates). Students then enter the virtual environment and test the validity and flexibility of their field plans by making real-time decisions about site selection (i.e., what outcrops to study in detail, and why) and sampling strategies (i.e., what samples to 'collect,' and how much they weigh). The “choose your own adventure” framework allows for embedding unexpected challenges related to weather, health and safety, and active decisions of how and where to spend time. Students keep “field notebooks” to document observations, evolving hypotheses, and modifications to original field plans. During the exercise, we encourage metacognition by guiding student articulation of reasons behind decision making, responses to unexpected challenges, and strengths and weaknesses of original field plans. After the exercise, we captured this cognitive growth through post-activity written reflections. Preliminary assessments using pre- and post-surveys and student products, including narrative reflections, indicate that this approach enhances students’ confidence in tackling complex, open-ended problems while fostering skills critical to real-world fieldwork. Leveraging iVFTs as fieldwork preparation tools has the potential to impact geoscience education by providing students with a safe, accessible, and effective platform to develop critical thinking, problem-solving, and field planning skills. Such skills are transferable both to in-person field experiences, and more broadly, to complex problem-solving.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".