1. Introduction to Course Architecture & Dual-Arm Design
RAD 225 integrates a multi-site, randomized controlled educational research framework to evaluate the efficacy of interactive biophysical simulation vs. standard benchmark laboratory curricula.
The 4 Research Cohort Groups
Students are allocated into four strictly balanced cohort groups based on gender and study arm:
- Group M1: Male Section • Simulation-Augmented Virtual Lab (Intervention Arm)
- Group M2: Male Control • Virtual Apparatus & Excel Calibrated Protocol (Control Arm)
- Group F1: Female Section • Simulation-Augmented Virtual Lab (Intervention Arm)
- Group F2: Female Control • Virtual Apparatus & Excel Calibrated Protocol (Control Arm)
Standardized 5-Tab Sequence
- Tab 1: Primer & Objectives: Review CLO foundations and experimental goals.
- Tab 2: Baseline Pre-Test: Locked once submitted; unblocks Tabs 3 & 4.
- Tab 3: Virtual Lab Activity: Simulation workbench or apparatus console.
- Tab 4: Guided Worksheet & Excel: Paired with Tab 3; qualitative synthesis and Excel data submission.
- Tab 5: Post-Test & TAM Survey: Final mastery post-test and 20-item usability survey.
2. Post-Lecture Baseline Pre-Test Mean
The Baseline Pre-Test Mean represents the average score achieved by a student or cohort on the 5-item multiple-choice baseline test administered immediately following the didactic lecture and before the student interacts with the laboratory experiment.
Pedagogical & Research Role:
- Baseline Equivalence: Pre-test scores establish that the Intervention (M1, F1) and Control (M2, F2) arms possess statistically equivalent prior knowledge ($p > 0.05$ on two-sample $t$-test) prior to beginning the lab.
- Diagnostic Screening: Highlights specific lecture concepts that were misunderstood or require reinforcement during laboratory facilitation.
- Ceiling Effect Detection: If baseline scores are exceptionally high ($>85\%$), normalized gain metrics (Hake's $g$) become critical to prevent underestimation of learning gains.
3. Knowledge Mastery Post-Test Mean
The Knowledge Mastery Post-Test Mean measures the student's mastery of the target Course Learning Outcomes (CLOs) after completing the virtual laboratory activity, guided worksheet, and Excel data analysis.
Research Implication: Directly evaluates whether the interactive simulation arm achieves statistically superior concept retention and cognitive transfer compared to traditional protocol instruction.
4. Absolute Learning Gain ($\Delta$)
The Absolute Learning Gain ($\Delta$) is the raw percentage-point difference between the post-test score and the baseline pre-test score:
$$\Delta = \text{PostScore} - \text{PreScore}$$
Limitation of Absolute Gain
Absolute gain is inherently biased against high-performing students who enter with high baseline scores:
- Student A: Pre = $20\%$, Post = $60\% \implies \Delta = +40\%$
- Student B: Pre = $80\%$, Post = $100\% \implies \Delta = +20\%$
Although Student B mastered $100\%$ of the material they had not previously known, their absolute gain is half of Student A's. To eliminate this ceiling bias, educational physics research utilizes Hake's Normalized Gain.
5. Hake's Normalized Learning Gain ($g$)
Introduced by Professor Richard Hake in his seminal 1998 study (American Journal of Physics, 66(1), 64–74), Normalized Gain ($g$) measures the fraction of the available conceptual improvement that the student actually achieved. It represents the ratio of the actual gain to the maximum possible gain.
Standard Hake Gain Classification Thresholds
| Category |
Normalized Gain Range |
Pedagogical Interpretation |
| High Gain |
$g \ge 0.70$ |
Exemplary Active Learning: Exceptional conceptual mastery; typical of highly interactive simulation and guided inquiry environments. |
| Medium Gain |
$0.30 \le g < 0.70$ |
Standard Effective Instruction: Sound active learning benefits; standard for well-designed interactive labs. |
| Low Gain |
$g < 0.30$ |
Traditional / Deficient: Typical of passive traditional lectures and cookbook-style rote memorization labs. |
Edge-Case Handling in the Platform:
- Ceiling Pre-Test ($ ext{Pre} = 100%$): If $ ext{Pre} = 100%$ and $ ext{Post} = 100%$, $g$ is defined as $1.000$ (perfect retention). If $ ext{Post} < 100%$, $g$ is $0.000$.
- Negative Gain ($ ext{Post} < ext{Pre}$): Mathematically negative gain indicates conceptual regression or disengagement. In the gradebook roster, this is highlighted in red to alert faculty.
6. Guided Inquiry Worksheet & Excel Assignment Evaluation
On Tab 4, students submit quantitative measurements, qualitative clinical rationales, and an uploaded Microsoft Excel data analysis workbook (.xlsx or .csv). The platform evaluates submissions against a 100-point rubric:
Total Worksheet Score (100 pts) =
Physical Calculations & Data Accuracy (30 pts)
+ Biophysical Mechanism Understanding (30 pts)
+ Clinical Radiation Protection (25 pts)
+ Microsoft Excel Workbook Submission (15 pts)
Component Breakdown
- Physical Calculations & Data Accuracy (30 Points): Parameters extracted from the simulation or calibrated apparatus are compared to verified physical ground truth within a $pm 15 - 20%$ tolerance window. Full credit ($10 ext{ pts}$ per parameter) is awarded if within tolerance; partial credit ($5 ext{ pts}$) for minor scaling errors.
- Biophysical Mechanism Understanding (30 Points): Evaluates inquiry responses for foundational domain concepts and causal mechanisms (clustered DNA lesions, water radiolysis radical kinetics, oxygen fixation hypothesis, stem cell kinetics, and LNT dose-response).
- Clinical Radiation Protection Synthesis (25 Points): Evaluates translation of biophysical principles into actionable radiology protection protocols (ALARA, pediatric grid removal, tube-under-table fluoroscopy geometry, pregnant patient counseling, structural shielding design).
- Microsoft Excel Analysis Workbook (15 Points): Confirms upload of a valid data spreadsheet containing proper column formulas, surviving fraction derivations, and regression trendlines.
7. Technology Acceptance Model (TAM) Usability Metrics
To assess user experience, cognitive ergonomics, and adoption hurdles, Tab 5 administers a validated Technology Acceptance Model (TAM) survey (Davis, 1989), specifically customized for medical radiation physics simulation platforms.
The 5-Point Likert Scale
Items are rated on a 1–5 scale: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.
The 5 TAM Subscales (4 Items Each = 20 Items Total)
| Subscale |
Construct Definition |
Target Benchmark |
| Perceived Usefulness (PU) |
Degree to which student believes the virtual lab enhanced their mastery of radiation biology and physics. |
$\ge 4.20 / 5.00$ |
| Perceived Ease of Use (PEOU) |
Degree to which student found controls and workflows intuitive and free of unnecessary cognitive friction. |
$\ge 4.00 / 5.00$ |
| Educational Content & Inquiry (ECI) |
Perceived scientific rigor, clarity of instructions, and alignment of questions with course CLOs. |
$\ge 4.00 / 5.00$ |
| Self-Efficacy & Confidence (SE) |
Student's confidence in performing radiobiological calculations and clinical protection decisions independently. |
$\ge 3.80 / 5.00$ |
| Cognitive Relevance (CR) |
Perceived relevance of laboratory activities to their future clinical roles as diagnostic radiographers. |
$\ge 4.20 / 5.00$ |
$$\text{TAM}_{\text{overall}} = \frac{\text{PU} + \text{PEOU} + \text{ECI} + \text{SE} + \text{CR}}{5}$$
8. Cohort & Research Group Comparative Analytics
In the Instructor Console, the top statistics bar dynamically updates based on the selected Filter Scope & Metrics dropdown:
- All Accessible Groups: Displays combined cross-section cohort statistics.
- Group M1 / F1 (Simulation Arm) vs. Group M2 / F2 (Control Arm): Enables side-by-side comparison of active simulation vs. benchmark apparatus data.
Statistical Tests Executed for Publication
When downloading the dataset via "📥 Export Research Dataset (CSV)", the analytical pipeline evaluates:
- Two-Sample Independent $t$-Test:
$$\text{Compares post-test scores and normalized gains: } t = \frac{\overline{X}_1 - \overline{X}_2}{\sqrt{\frac{s_1^2}{N_1} + \frac{s_2^2}{N_2}}}$$
- Effect Size (Cohen's $d$):
$$d = \frac{\overline{X}_{\text{sim}} - \overline{X}_{\text{ctrl}}}{s_{\text{pooled}}}$$
Interpretation: $d \ge 0.80$ (Large effect), $0.50 \le d < 0.80$ (Moderate), $d < 0.50$ (Small).
- Cronbach's Alpha ($\alpha$): Evaluates internal consistency reliability across the 20 TAM items (target $\alpha \ge 0.85$).
9. Instructor Operations: Tab Resets & Gradebook Management
How to Check Individual Student Lab Results
- In the Student Roster & Live Experimental Gradebook table, locate the student row.
- Click the "📊 Labs (9)" button or click the student's name/ID link.
- The "Individual Student Lab Records & Tab Management" modal opens, displaying a comprehensive breakdown of all 9 laboratory modules:
- Lab Title & Lecture Mapping
- Tab 2 Pre-Test: Score, percentage, and submission timestamp.
- Tab 4 Guided Worksheet: Rubric score, uploaded Excel filename, and timestamp.
- Tab 5 Post-Test: Raw score, percentage, and submission timestamp.
- Tab 5 TAM Usability: Average Likert score out of $5.00$.
- Normalized Gain ($g$): Color-coded by Hake threshold (Green: $ge 0.70$, Amber: $0.30-0.69$, Red: $<0.30$).
Resetting a Submitted Tab (Admin Action)
If a student encounters a technical disruption, makes an accidental submission, or is granted an approved re-attempt:
- Open the student's "📊 Labs (9)" modal.
- Locate the specific lab and tab you wish to clear:
- 🔄 Reset Pre-Test: Clears Tab 2 (and cascadingly unlocks Tabs 3, 4, and 5 for that lab).
- 🔄 Reset Worksheet: Clears Tab 4 (and cascadingly unlocks Tab 5).
- 🔄 Reset Post-Test: Clears Tab 5 Post-Test.
- 🔄 Reset TAM: Clears Tab 5 Usability survey.
- ⚠️ Reset Lab: Completely clears all records for that lab, returning the module to pristine initial status.
- Confirm the on-screen modal prompt.
- The database records are purged in an atomic transaction, the modal refreshes instantly, and the student's workstation is immediately unlocked to re-attempt the activity.
RAD 225 Virtual Laboratory Platform • Department of Diagnostic Radiology, College of Applied Medical Sciences, Taibah University