Authors - Jes Maries M. Mendez, Max Angelo D. Perin, Joan Mae G. Lagumbay, Mae S. Dagupan, Elizabeth A. Orapa, Marcelina S. Butlig Abstract - Educational tours are widely used in higher education to connect class-room learning with real settings, yet evaluations often stop at overall ratings that do not explain why students endorse a tour or which delivery issues weaken the experience. This study applies a student experience intelligence workflow that integrates survey analytics with offline text mining to produce planning-relevant evidence. A survey of 156 students captured demographics, three 10-item Likert constructs—motivation, perceived effectiveness, and problems encountered (4-point scale)—a recommendation rating, and open-ended comments. Responses were cleaned through category standardization and rule-based numeric conversion. Internal consistency was good for motivation (α = 0.877) and excellent for effectiveness (α = 0.960) and problems (α = 0.958). Learning beyond classroom instruction (M = 3.71) and interest in tour inclusions (M = 3.68) led motivation; creative learning (M = 3.67), resourcefulness (M = 3.66), and social skills (M = 3.65) led effectiveness; tour expense (M = 3.21) and short time per attraction (M = 2.60) led problems. 73.1% gave the top recommendation. Recommendation correlated positively with motivation (ρ = 0.317, p < 0.001) and effectiveness (ρ = 0.328, p < 0.001); a binary logistic model showed perceived effectiveness as the strongest predictor of the top recommendation category. Open-ended comments (171 entries) were summarized through TF–IDF with K-Means clustering (k = 6) and complemented with a VADER polarity pass on 155 meaningful entries (68.4% positive, 21.9% neutral, 9.7% negative; mean compound = +0.365). The combined evidence points to improvements that preserve educational value while addressing cost and pacing, and shows that the workflow is portable to other programs and experiential learning activities.
Open Zoom