cs.HCOct 7, 2026
SaveTutorLoop: Regulating Student Learning Behaviors via Sensor-in-the-Loop Generative Feedback
Organizations: The Hong Kong Polytechnic University Hong Kong, China · University of California, San Diego San Diego, California, USA
Abstract
We present TutorLoop, a sensor-in-the-loop system that regulates student learning behaviors by delivering adaptive feedback based on real-time cognitive states. Unlike prior large language model (LLM) tutors that directly depend on scenario-specific content, TutorLoop operates on sensor-derived signals captured via webcams. Moreover, unlike direct cognitive-to-feedback mappings that are short-sighted, the system employs a deep reinforcement learning (DRL) agent to optimize the feedback type across the entire learning process. Finally, another LLM tutor refines feedback into human-like, context-aware messages. We evaluate TutorLoop in a large-scale user study (N=187), where a model trained offline is directly applied to a new learning task without retraining. Results show that TutorLoop provides less frequent yet more effective interventions, improving attention, reducing workload, increasing engagement, and ultimately enhancing learning outcomes. These findings highlight the potential of closed-loop, sensor-driven feedback for scalable human-AI integrated systems to support learning.
Figures & tables
Figure 1. Our framework. TutorLoop leverages students’ real-time cognitive states, captured via webcams, to regulate learning behaviors and enhance outcomes. A DRL model controls an LLM tutor to generate adaptive feedback. The DRL model is first trained with the student simulator and then directly applied to real students in new learning tasks without model updates. A schematic diagram shows the TutorGen framework in two parallel paths: on the left, simulated student cognitive states flow from the student simulator into a DRL model, which then controls LLM-generated textual feedback; on the right, real students’ cognitive states captured by webcams are processed in real time by the same DRL-controlled LLM tutor to provide feedback. The diagram emphasizes that the DRL model is first trained virtually with the student simulator and then directly applied to real students without further updates.
Figure 2. The TutorLoop framework details. Note that course stimuli are only used to stimulate real/simulated students to generate cognitive states, but the DRL model does not need course stimuli as input. A block diagram details the full framework flow. On the left, public datasets provide input to the student simulator, which simulates cognitive states. These states feed into a DRL training pipeline (actor-critic PPO model) with buffer states, actions (feedback types), and rewards. The pipeline shows how contextualization, tone translation, and humanization modules in the LLM tutor refine feedback. On the right, arrows extend to real student application with webcam-based sensing.
Figure 3. Details of our LLM tutor to refine the feedback, selected by the DRL model. A layered diagram illustrates the LLM tutor refinement process. At the top, three core actions (attentive, relief, encouragement feedback) are shown with example messages such as “Try to be more attentive in learning!” and “Your engagement is great! Keep your engagement!” These are stored in a feedback history memory. Refinement modules labeled “Humanize Feedback,” “Contextualize Feedback,” and “Feedback Tone Translator” process the feedback, producing varied tones such as concise, friendly, or playful. An example refined relief feedback reads: “I can sense that you might be feeling a bit overwhelmed. Taking a deep breath can help you relax and regain your focus as you continue with the slides. Give it a try!”
Figure 4. Ten slides in our learning task in the user study. A grid of ten slides shows the learning task materials. Each slide presents one concept about the Earth along with three supporting details. All slides (a–j) include visual illustrations and text descriptions such as geological processes, ecosystems, or atmospheric concepts. Each slide contains both text and images designed for comprehension.
Figure 5. Task settings (learning session: top, testing session: bottom) in the user study. Screenshots of the user study platform. The top panel shows the learning session interface with a slide displayed at the center, navigation buttons left and right, a timer at the bottom, and feedback text appearing above the slide. The bottom panel shows the testing session interface with a blank text box for recalling concepts and details, also with a countdown timer displayed.
Figure 6. Results in our user study. A multi-panel figure showing user study results. Bar plots compare Control, TutorUp, DRL Only, and Full groups across metrics. (a) Concept hit ratio: Control ~0.61, TutorUp ~0.62, DRL Only ~0.63, Full ~0.66. (b) Detail hit ratio: Control ~0.36, TutorUp ~0.39, DRL Only ~0.39, Full ~0.45. (c) Semantic similarity: Control ~0.23, TutorUp ~0.25, DRL Only ~0.25, Full ~0.28. (d) Lexical similarity: Control ~0.13, TutorUp ~0.14, DRL Only ~0.13, Full ~0.15. (e) Attention: Control ~0.35, TutorUp ~0.39, DRL Only ~0.27, Full ~0.74. (f) Workload: Control ~0.17, TutorUp ~0.09, DRL Only ~0.29, Full ~0.10. (g) Slide switch ratio: Control ~0.09, TutorUp ~0.06, DRL Only ~0.07, Full ~0.09. (h) Feedback presence ratio: Control 0, TutorUp ~0.28, DRL Only ~0.32, Full ~0.14. Line plots (i–l) show time trends of attention, workload, slide switching, and feedback presence over 15 interval blocks. Scatter plots (m–p) show correlations: attention vs. semantic similarity (r = 0.017), workload vs. similarity (r = −0.168), slide switching vs. similarity (r = 0.173), and feedback presence vs. similarity (r = 0.010).
Appendix figures & tables16 assets
Supplementary material from the paper’s appendix.
Appendix
| Group | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Intercept | 0.739 | 0.015 | 50.036 | 0.000 | 0.710 | 0.768 |
| TutorUp | -0.353 | 0.175 | -2.010 | 0.044 | -0.697 | -0.009 |
| Control | -0.392 | 0.159 | -2.471 | 0.013 | -0.703 | -0.081 |
| DRL Only | -0.472 | 0.156 | -3.037 | 0.002 | -0.777 | -0.168 |
Table 1. Statistical Results (Attention) Statistical results for attention. Intercept = 0.739 (SE 0.015, z = 50.036, p = 0.000, CI [0.710, 0.768]). TutorUp = −0.353 (SE 0.175, z = −2.010, p = 0.044, CI [−0.697, −0.009]). Control = −0.392 (SE 0.159, z = −2.471, p = 0.013, CI [−0.703, −0.081]). DRL Only = −0.472 (SE 0.156, z = −3.037, p = 0.002, CI [−0.777, −0.168]).
| Comparison | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Control vs DRL Only | 0.081 | 0.229 | 0.352 | 0.725 | -0.369 | 0.530 |
| Control vs Full | -0.392 | 0.159 | -2.471 | 0.013 | -0.703 | -0.081 |
| Control vs TutorUp | -0.039 | 0.229 | -0.171 | 0.864 | -0.488 | 0.410 |
| DRL Only vs Full | -0.473 | 0.156 | -3.037 | 0.002 | -0.777 | -0.168 |
| DRL Only vs TutorUp | -0.120 | 0.231 | -0.519 | 0.604 | -0.572 | 0.333 |
| Full vs TutorUp | 0.353 | 0.175 | 2.010 | 0.044 | 0.009 | 0.697 |
Table 2. Pairwise Comparisons (Attention) Pairwise comparisons for attention. Control vs DRL Only = 0.081 (SE 0.229, z = 0.352, p = 0.725, CI [−0.369, 0.530]). Control vs Full = −0.392 (SE 0.159, z = −2.471, p = 0.013, CI [−0.703, −0.081]). Control vs TutorUp = −0.039 (SE 0.229, z = −0.171, p = 0.864, CI [−0.488, 0.410]). DRL Only vs Full = −0.473 (SE 0.156, z = −3.037, p = 0.002, CI [−0.777, −0.168]). DRL Only vs TutorUp = −0.120 (SE 0.231, z = −0.519, p = 0.604, CI [−0.572, 0.333]). Full vs TutorUp = 0.353 (SE 0.175, z = 2.010, p = 0.044, CI [0.009, 0.697]).
| Group | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Intercept | 0.102 | 0.028 | 3.627 | 0.000 | 0.047 | 0.158 |
| TutorUp | -0.014 | 0.059 | -0.232 | 0.817 | -0.130 | 0.102 |
| Control | 0.066 | 0.039 | 1.674 | 0.094 | -0.011 | 0.143 |
| DRL Only | 0.188 | 0.049 | 3.801 | 0.000 | 0.091 | 0.285 |
Table 3. Statistical Results (Workload) Statistical results for workload. Intercept = 0.102 (SE 0.028, z = 3.627, p = 0.000, CI [0.047, 0.158]). TutorUp = −0.014 (SE 0.059, z = −0.232, p = 0.817, CI [−0.130, 0.102]). Control = 0.066 (SE 0.039, z = 1.674, p = 0.094, CI [−0.011, 0.143]). DRL Only = 0.188 (SE 0.049, z = 3.801, p = 0.000, CI [0.091, 0.285]).
| Comparison | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Control vs DRL Only | -0.122 | 0.084 | -1.454 | 0.146 | -0.287 | 0.043 |
| Control vs Full | 0.066 | 0.039 | 1.674 | 0.094 | -0.011 | 0.143 |
| Control vs TutorUp | 0.080 | 0.083 | 0.957 | 0.339 | -0.083 | 0.242 |
| DRL Only vs Full | 0.188 | 0.049 | 3.801 | 0.000 | 0.091 | 0.285 |
| DRL Only vs TutorUp | 0.202 | 0.085 | 2.381 | 0.017 | 0.036 | 0.368 |
| Full vs TutorUp | 0.014 | 0.059 | 0.232 | 0.817 | -0.102 | 0.130 |
Table 4. Pairwise Comparisons (Workload) Pairwise comparisons for workload. Control vs DRL Only = −0.122 (SE 0.084, z = −1.454, p = 0.146, CI [−0.287, 0.043]). Control vs Full = 0.066 (SE 0.039, z = 1.674, p = 0.094, CI [−0.011, 0.143]). Control vs TutorUp = 0.080 (SE 0.083, z = 0.957, p = 0.339, CI [−0.083, 0.242]). DRL Only vs Full = 0.188 (SE 0.049, z = 3.801, p = 0.000, CI [0.091, 0.285]). DRL Only vs TutorUp = 0.202 (SE 0.085, z = 2.381, p = 0.017, CI [0.036, 0.368]). Full vs TutorUp = 0.014 (SE 0.059, z = 0.232, p = 0.817, CI [−0.102, 0.130]).
| Group | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Intercept | 0.092 | 0.002 | 43.045 | 0.000 | 0.088 | 0.096 |
| TutorUp | -0.027 | 0.008 | -3.434 | 0.001 | -0.043 | -0.012 |
| Control | -0.006 | 0.008 | -0.714 | 0.475 | -0.022 | 0.010 |
| DRL Only | -0.016 | 0.005 | -3.072 | 0.002 | -0.027 | -0.006 |
Table 5. Statistical Results (Slide Switch Ratio) Statistical results for slide switch ratio. Intercept = 0.092 (SE 0.002, z = 43.045, p = 0.000, CI [0.088, 0.096]). TutorUp = −0.027 (SE 0.008, z = −3.434, p = 0.001, CI [−0.043, −0.012]). Control = −0.006 (SE 0.008, z = −0.714, p = 0.475, CI [−0.022, 0.010]). DRL Only = −0.016 (SE 0.005, z = −3.072, p = 0.002, CI [−0.027, −0.006]).
| Comparison | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Control vs DRL Only | 0.010 | 0.010 | 1.031 | 0.303 | -0.009 | 0.030 |
| Control vs Full | -0.006 | 0.008 | -0.714 | 0.475 | -0.022 | 0.010 |
| Control vs TutorUp | 0.021 | 0.010 | 2.049 | 0.040 | 0.001 | 0.042 |
| DRL Only vs Full | -0.016 | 0.005 | -3.072 | 0.002 | -0.027 | -0.006 |
| DRL Only vs TutorUp | 0.011 | 0.010 | 1.065 | 0.287 | -0.009 | 0.031 |
| Full vs TutorUp | 0.027 | 0.008 | 3.434 | 0.001 | 0.012 | 0.043 |
Table 6. Pairwise Comparisons (Slide Switch Ratio) Pairwise comparisons for slide switch ratio. Control vs DRL Only = 0.010 (SE 0.010, z = 1.031, p = 0.303, CI [−0.009, 0.030]). Control vs Full = −0.006 (SE 0.008, z = −0.714, p = 0.475, CI [−0.022, 0.010]). Control vs TutorUp = 0.021 (SE 0.010, z = 2.049, p = 0.040, CI [0.001, 0.042]). DRL Only vs Full = −0.016 (SE 0.005, z = −3.072, p = 0.002, CI [−0.027, −0.006]). DRL Only vs TutorUp = 0.011 (SE 0.010, z = 1.065, p = 0.287, CI [−0.009, 0.031]). Full vs TutorUp = 0.027 (SE 0.008, z = 3.434, p = 0.001, CI [0.012, 0.043]).
| Group | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Intercept | 0.140 | 0.001 | 152.196 | 0.000 | 0.138 | 0.142 |
| TutorUp | 0.137 | 0.004 | 32.302 | 0.000 | 0.129 | 0.145 |
| Control | -0.140 | 0.004 | -37.283 | 0.000 | -0.147 | -0.132 |
| DRL Only | 0.183 | 0.003 | 62.697 | 0.000 | 0.177 | 0.188 |
Table 7. Statistical Results (Feedback Presence Ratio) Statistical results for feedback presence ratio. Intercept = 0.140 (SE 0.001, z = 152.196, p = 0.000, CI [0.138, 0.142]). TutorUp = 0.137 (SE 0.004, z = 32.302, p = 0.000, CI [0.129, 0.145]). Control = −0.140 (SE 0.004, z = −37.283, p = 0.000, CI [−0.147, −0.132]). DRL Only = 0.183 (SE 0.003, z = 62.697, p = 0.000, CI [0.177, 0.188]).
| Comparison | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Control vs DRL Only | -0.322 | 0.005 | -62.763 | 0.000 | -0.332 | -0.312 |
| Control vs Full | -0.140 | 0.004 | -37.283 | 0.000 | -0.147 | -0.132 |
| Control vs TutorUp | -0.277 | 0.005 | -53.511 | 0.000 | -0.287 | -0.267 |
| DRL Only vs Full | 0.183 | 0.003 | 62.697 | 0.000 | 0.177 | 0.188 |
| DRL Only vs TutorUp | 0.046 | 0.005 | 8.978 | 0.000 | 0.036 | 0.056 |
| Full vs TutorUp | -0.137 | 0.004 | -32.302 | 0.000 | -0.145 | -0.129 |
Table 8. Pairwise Comparisons (Feedback Presence Ratio) Pairwise comparisons for feedback presence ratio. Control vs DRL Only = −0.322 (SE 0.005, z = −62.763, p = 0.000, CI [−0.332, −0.312]). Control vs Full = −0.140 (SE 0.004, z = −37.283, p = 0.000, CI [−0.147, −0.132]). Control vs TutorUp = −0.277 (SE 0.005, z = −53.511, p = 0.000, CI [−0.287, −0.267]). DRL Only vs Full = 0.183 (SE 0.003, z = 62.697, p = 0.000, CI [0.177, 0.188]). DRL Only vs TutorUp = 0.046 (SE 0.005, z = 8.978, p = 0.000, CI [0.036, 0.056]). Full vs TutorUp = −0.137 (SE 0.004, z = −32.302, p = 0.000, CI [−0.145, −0.129]).
| Group | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Intercept | 0.662 | 0.006 | 111.460 | 0.000 | 0.650 | 0.673 |
| TutorUp | -0.042 | 0.028 | -1.494 | 0.135 | -0.097 | 0.013 |
| Control | -0.053 | 0.025 | -2.153 | 0.031 | -0.102 | -0.005 |
| DRL Only | -0.036 | 0.021 | -1.697 | 0.090 | -0.077 | 0.006 |
Table 9. Statistical Results (Concept Hit Ratio) Statistical results for concept hit ratio. Intercept = 0.662 (SE 0.006, z = 111.460, p = 0.000, CI [0.650, 0.673]). TutorUp = −0.042 (SE 0.028, z = −1.494, p = 0.135, CI [−0.097, 0.013]). Control = −0.053 (SE 0.025, z = −2.153, p = 0.031, CI [−0.102, −0.005]). DRL Only = −0.036 (SE 0.021, z = −1.697, p = 0.090, CI [−0.077, 0.006]).
| Comparison | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Control vs DRL Only | -0.018 | 0.038 | -0.473 | 0.636 | -0.091 | 0.056 |
| Control vs Full | -0.053 | 0.025 | -2.153 | 0.031 | -0.102 | -0.005 |
| Control vs TutorUp | -0.011 | 0.038 | -0.299 | 0.765 | -0.085 | 0.062 |
| DRL Only vs Full | -0.036 | 0.021 | -1.697 | 0.090 | -0.077 | 0.006 |
| DRL Only vs TutorUp | 0.007 | 0.038 | 0.174 | 0.862 | -0.067 | 0.080 |
| Full vs TutorUp | 0.042 | 0.028 | 1.494 | 0.135 | -0.013 | 0.097 |
Table 10. Pairwise Comparisons (Concept Hit Ratio) Pairwise comparisons for concept hit ratio. Control vs DRL Only = −0.018 (SE 0.038, z = −0.473, p = 0.636, CI [−0.091, 0.056]). Control vs Full = −0.053 (SE 0.025, z = −2.153, p = 0.031, CI [−0.102, −0.005]). Control vs TutorUp = −0.011 (SE 0.038, z = −0.299, p = 0.765, CI [−0.085, 0.062]). DRL Only vs Full = −0.036 (SE 0.021, z = −1.697, p = 0.090, CI [−0.077, 0.006]). DRL Only vs TutorUp = 0.007 (SE 0.038, z = 0.174, p = 0.862, CI [−0.067, 0.080]). Full vs TutorUp = 0.042 (SE 0.028, z = 1.494, p = 0.135, CI [−0.013, 0.097]).
| Group | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Intercept | 0.448 | 0.001 | 356.892 | 0.000 | 0.445 | 0.450 |
| TutorUp | -0.054 | 0.021 | -2.634 | 0.008 | -0.094 | -0.014 |
| Control | -0.089 | 0.020 | -4.416 | 0.000 | -0.129 | -0.050 |
| DRL Only | -0.061 | 0.020 | -3.069 | 0.002 | -0.100 | -0.022 |
Table 11. Statistical Results (Detail Hit Ratio) Statistical results for detail hit ratio. Intercept = 0.448 (SE 0.001, z = 356.892, p = 0.000, CI [0.445, 0.450]). TutorUp = −0.054 (SE 0.021, z = −2.634, p = 0.008, CI [−0.094, −0.014]). Control = −0.089 (SE 0.020, z = −4.416, p = 0.000, CI [−0.129, −0.050]). DRL Only = −0.061 (SE 0.020, z = −3.069, p = 0.002, CI [−0.100, −0.022]).
| Comparison | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Control vs DRL Only | -0.028 | 0.029 | -0.948 | 0.343 | -0.086 | 0.030 |
| Control vs Full | -0.089 | 0.020 | -4.416 | 0.000 | -0.129 | -0.050 |
| Control vs TutorUp | -0.035 | 0.029 | -1.194 | 0.232 | -0.093 | 0.023 |
| DRL Only vs Full | -0.061 | 0.020 | -3.069 | 0.002 | -0.100 | -0.022 |
| DRL Only vs TutorUp | -0.007 | 0.030 | -0.244 | 0.808 | -0.066 | 0.051 |
| Full vs TutorUp | 0.054 | 0.021 | 2.634 | 0.008 | 0.014 | 0.094 |
Table 12. Pairwise Comparisons (Detail Hit Ratio) Pairwise comparisons for detail hit ratio. Control vs DRL Only = −0.028 (SE 0.029, z = −0.948, p = 0.343, CI [−0.086, 0.030]). Control vs Full = −0.089 (SE 0.020, z = −4.416, p = 0.000, CI [−0.129, −0.050]). Control vs TutorUp = −0.035 (SE 0.029, z = −1.194, p = 0.232, CI [−0.093, 0.023]). DRL Only vs Full = −0.061 (SE 0.020, z = −3.069, p = 0.002, CI [−0.100, −0.022]). DRL Only vs TutorUp = −0.007 (SE 0.030, z = −0.244, p = 0.808, CI [−0.066, 0.051]). Full vs TutorUp = 0.054 (SE 0.021, z = 2.634, p = 0.008, CI [0.014, 0.094]).
| Group | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Intercept | 0.284 | 0.003 | 94.678 | 0.000 | 0.278 | 0.290 |
| TutorUp | -0.033 | 0.012 | -2.769 | 0.006 | -0.057 | -0.010 |
| Control | -0.051 | 0.013 | -4.052 | 0.000 | -0.075 | -0.026 |
| DRL Only | -0.039 | 0.014 | -2.768 | 0.006 | -0.067 | -0.011 |
Table 13. Statistical Results (Semantic Similarity) Statistical results for semantic similarity. Intercept = 0.284 (SE 0.003, z = 94.678, p = 0.000, CI [0.278, 0.290]). TutorUp = −0.033 (SE 0.012, z = −2.769, p = 0.006, CI [−0.057, −0.010]). Control = −0.051 (SE 0.013, z = −4.052, p = 0.000, CI [−0.075, −0.026]). DRL Only = −0.039 (SE 0.014, z = −2.768, p = 0.006, CI [−0.067, −0.011]).
| Comparison | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Control vs DRL Only | -0.011 | 0.020 | -0.568 | 0.570 | -0.051 | 0.028 |
| Control vs Full | -0.051 | 0.013 | -4.052 | 0.000 | -0.075 | -0.026 |
| Control vs TutorUp | -0.018 | 0.020 | -0.867 | 0.386 | -0.057 | 0.022 |
| DRL Only vs Full | -0.039 | 0.014 | -2.768 | 0.006 | -0.067 | -0.011 |
| DRL Only vs TutorUp | -0.006 | 0.020 | -0.298 | 0.766 | -0.046 | 0.034 |
| Full vs TutorUp | 0.033 | 0.012 | 2.769 | 0.006 | 0.010 | 0.057 |
Table 14. Pairwise Comparisons (Semantic Similarity) Pairwise comparisons for semantic similarity. Control vs DRL Only = −0.011 (SE 0.020, z = −0.568, p = 0.570, CI [−0.051, 0.028]). Control vs Full = −0.051 (SE 0.013, z = −4.052, p = 0.000, CI [−0.075, −0.026]). Control vs TutorUp = −0.018 (SE 0.020, z = −0.867, p = 0.386, CI [−0.057, 0.022]). DRL Only vs Full = −0.039 (SE 0.014, z = −2.768, p = 0.006, CI [−0.067, −0.011]). DRL Only vs TutorUp = −0.006 (SE 0.020, z = −0.298, p = 0.766, CI [−0.046, 0.034]). Full vs TutorUp = 0.033 (SE 0.012, z = 2.769, p = 0.006, CI [0.010, 0.057]).
| Group | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Intercept | 0.151 | 0.000 | 340.990 | 0.000 | 0.150 | 0.152 |
| TutorUp | -0.016 | 0.010 | -1.527 | 0.127 | -0.036 | 0.004 |
| Control | -0.021 | 0.010 | -2.186 | 0.029 | -0.040 | -0.002 |
| DRL Only | -0.019 | 0.010 | -1.967 | 0.049 | -0.038 | -0.000 |
Table 15. Statistical Results (Lexical Similarity) Statistical results for lexical similarity. Intercept = 0.151 (SE 0.000, z = 340.990, p = 0.000, CI [0.150, 0.152]). TutorUp = −0.016 (SE 0.010, z = −1.527, p = 0.127, CI [−0.036, 0.004]). Control = −0.021 (SE 0.010, z = −2.186, p = 0.029, CI [−0.040, −0.002]). DRL Only = −0.019 (SE 0.010, z = −1.967, p = 0.049, CI [−0.038, −0.000]).
| Comparison | Coef. | Std.Err. | z | P>|z| | [0.025 | 0.975] |
| Control vs DRL Only | -0.002 | 0.014 | -0.147 | 0.883 | -0.029 | 0.025 |
| Control vs Full | -0.021 | 0.010 | -2.186 | 0.029 | -0.040 | -0.002 |
| Control vs TutorUp | -0.006 | 0.014 | -0.411 | 0.681 | -0.033 | 0.021 |
| DRL Only vs Full | -0.019 | 0.010 | -1.967 | 0.049 | -0.038 | 0.000 |
| DRL Only vs TutorUp | -0.004 | 0.014 | -0.261 | 0.794 | -0.031 | 0.024 |
| Full vs TutorUp | 0.016 | 0.010 | 1.527 | 0.127 | -0.004 | 0.036 |
Table 16. Pairwise Comparisons (Lexical Similarity) Pairwise comparisons for lexical similarity. Control vs DRL Only = −0.002 (SE 0.014, z = −0.147, p = 0.883, CI [−0.029, 0.025]). Control vs Full = −0.021 (SE 0.010, z = −2.186, p = 0.029, CI [−0.040, −0.002]). Control vs TutorUp = −0.006 (SE 0.014, z = −0.411, p = 0.681, CI [−0.033, 0.021]). DRL Only vs Full = −0.019 (SE 0.010, z = −1.967, p = 0.049, CI [−0.038, 0.000]). DRL Only vs TutorUp = −0.004 (SE 0.014, z = −0.261, p = 0.794, CI [−0.031, 0.024]). Full vs TutorUp = 0.016 (SE 0.010, z = 1.527, p = 0.127, CI [−0.004, 0.036]).