An Interactive Learning System for Improving Student's - Hong ...

An Interactive Learning System for Improving Student’s Learning Experience in Mass Lectures

Simon C. Lam

IROPINE member;

Associate Professor,

Division of Nursing and Health Studies,

The Open University of Hong Kong.

An Interactive Learning System for Improving Student’s Learning Experience in Mass Lectures

As indicated, several key terms… inform the below outline:

A. Background – Mass lectures and interactive learning system

– Measurement of learning experience

B. Methods

C. Results

D. Discussion

E. Conclusion

2

A. Background: Mass lectures and

interactive learning system

• Mass lecture – a common practice in University education (Du-Babcock, 2002; Yung, 2015).

3



• Mass lecture – a common practice in University education (Du-Babcock, 2002; Yung, 2015).

– Advantages: • Effective delivery of the content to large no. of students (usually >100)

• Consistent in delivery standard content

– Shortcomings: • Lack of teacher-student interaction

• Difficult of pacing students’ learning

• Difficult to assess students’ knowledge acquisition instantly

• Difficult to assess students’ engagement & participation

4



– Shortcomings:

• Lack of teacher-student interaction

• Difficult of pacing students’ learning

• Difficult to assess students’ knowledge acquisition instantly

• Difficult to assess students’ engagement & participation

• Some solutions: in-class polling, exercises or quizzes for monitoring knowledge acquisition…

• But…

– instant feedback is always infeasible…

– engagement is difficult to be assessed

5



• Technological based instant response system – Student Response System (SRS)

– An instructional technology that offers great promise for allowing individualized formative feedback in large classrooms (Hoffman & Goodwin 2006).

– Increase student feedback (Jones et. al. 2012)

– Improve student engagement in a large classroom setting (Jones et. al. 2012)

– Increase teacher-students interaction (Blasco-Arcas et.

al. 2013)

6



• SRS components: – Server: data processing

– Receiver device: connecting to the server

– Software

– Handheld remote: for every students to input their choices and view the results (optional)

• International & local experience on SRS – From 2012 – 2017, 98 publications adopted “student response system” as title.

– Favorable result on various areas: engagement, active learning, knowledge, deeper learning, motivation …

– Some examples as below: • Bachman, L., & Bachman, C. (2011). A study of classroom response system clickers: Increasing student engagement and performance

in a large undergraduate lecture class on architectural research. Journal of Interactive Learning Research, 22(1), 5.

• Efstathiou, N., & Bailey, C. (2012). Promoting active learning using audience response system in large bioscience classes. Nurse Education Today, 32(1), 91-95.

• Lloyd, J. E. (2014). Inverting a non-major’s biology class: using video lectures, online resources, and a student response system to facilitate deeper learning. Journal of Teaching and Learning with Technology, 3(2), 31-39.

• Jones, M. E., Antonenko, P. D., & Greenwood, C. M. (2012). The impact of collaborative and individualized student response system strategies on learner motivation, metacognition, and knowledge transfer. Journal of Computer Assisted Learning, 28(5), 477-487.

7

A. Learning experience: Knowledge

• Multiple-choice questions: Acquisition & retention of knowledge (Ausubel, 2000)

– Truly assess the knowledge

– Can differentiate the students of different abilities in correct manner (Gajjar, 2014)

• Bloom’s taxonomy

– Understanding

– Remembering

8

A. . Learning experience: Engagement

• Engagement

– Predicting:

• Academic performance (Handelsman, Briggs, Sullivan, & Towler, 2005; Kuh, Cruce, Shoup, Kinzie, &

Gonyea, 2008; Seckman, 2014; Skinner, Wellborn, & Connell, 1990)

• School completion & motivation (Appleton, Christenson, & Furlong, 2008; Horstmanshof &

Zimitat, 2007; Salamonson, Andrew, & Everett, 2009)

– As important factor that leads to educational reform and evaluation (Coates, 2010; Kuh, 2003)

– 3-dimensional construct of engagement (Fredricks & Paris,

2004)

• Including: Behavioral, emotional & cognitive dimensions

• Widely accepted model

9

B. Methods

• Aim: To evaluate students’ knowledge acquisition and engagement with the practice of SRS.

• Design: Descriptive study with repeated measures for 3 times

• Participants: A convenience sampling of ~200 yr. 1 students in Higher Diploma in Nursing studies in 2015-16 and 2016-17.

10

B. Methods: Questionnaires • 3 knowledge-based questions (Pre-test & Post-test)

– To access students’ knowledge acquisition during the lecture • 3 questions on reproductive system

– To access students’ knowledge acquisition 1-week after the lecture • 3 questions on immunity & pharmacology.

– 1 point for each correct answer (0-3 point(s))

• 4-item Generic Student Engagement Scale (GSES) – To evaluate students’ engagement by the use of SRS – Rated on 5-point Likert scale (1 to 5 point, from very

disagree to very agree) – Brief, reliable and valid (CFA: χ2/df= 1.56, CFI=0.99, TLI=0.99, RMSEA=0.053)

(Chong et. la., 2016)

11

B. Methods: Data collection Convenience sampling:

233 Yr.1 nursing students in lecture

At the beginning of the Lecture:

Pre-test (3 Knowledge-based questions)

Lecturing

At the end of the Lecture:

Post-test (3 Knowledge-based questions + 4-item GSES )

One-week later:

Re-test (3 knowledge-based questions)

12

2016 students

2015 students

C. Results • Knowledge acquisition:

– Student performance: • 2015 reproductive system (N = 233)

– Pre-test: Mean score = 1.81 (SD 0.87)

– Post-test: Mean score = 2.53 (SD 0.63)

» Paired t-test: t=-11.43, p < 0.001

• 2016 Immunity (N = 232) – Test: Mean score = 1.67 (SD 0.86)

– 1-week re-test: Mean score = 1.94 (SD 0.90)

» Paired t-test: t=-4.08, p < 0.001

• 2016 Pharmacology (N =227) – Test: Mean score = 0.93 (SD 0.82)

– 1-week re-test: Mean score = 1.84 (SD 1.01)

» Paired t-test: t=-10.88, p < 0.001 13

C. Results- sample from 2015 students Pre-test

Post-test

14

C. Results- sample from 2016 students

15

Test

1-week re-test

C. Results • Student engagement:

4-item GSES 2015 students 2016 students

Item Dimensions (1=very agree, 5=very dis.)

Mean (SD) % of non-negative responses

Mean (SD)

% of non-negative responses

Q1 Behavioral engagement 2.77 (0.78) 89.01% 2.11 (1.05) 93.85%

Q2 Emotional engagement 2.68 (0.81) 89.74% 2.17 (1.08) 92.31%

Q3 Cognitive engagement 2.58 (0.74) 93.41% 1.99 (1.01) 95.77%

Q4 Overall engagement 2.61 (0.77) 91.57% 2.10 (1.06) 94.61%

16

D. Discussion • Pedagogical benefits:

– SRS originated as simple as the handheld whiteboards- facilitating the teachers to adjust their teaching plans accordingly

– SRS added the features of instantly display the pooled results- initiating students’ discussion

– SRS makes something possible in class • E.g., pre-instruction assessment, formative assessments,

comprehension checks …

• Our findings indicated that: – SRS as effective way to assess students’ knowledge and better

understanding the retention of those acquired knowledge. – Teacher-students interaction was improved with the use of SRS. – Students’ engagement favored with the adoption of SRS in

lecture.

17

D. Discussion

• Future studies: – Use control group to investigate whether SRS can

effectively enhance students’ knowledge acquisition.

– Assess the correlation between student engagement and knowledge acquisition

– Conduct similar studies in other lectures on various topics to further improve data reliability.

– Expand the use of SRS to nursing students in various year of studies to explore whether similar results can be achieved in foundation courses versus high-level courses.

18

E. Conclusion

• SRS has successfully established in the mass lectures.

• SRS can assess the students’ knowledge & instantly display the pooled results

• Students’ engagement favored with the adoption of SRS in lecture

19

Acknowledgements

• Educational Technology & Publishing Unit, OUHK

– Dr. Eva Tsang

– Mr. Nelson Lee

• Division of Nursing & Health Studies, OUHK

– NURS S101F & S102CF teaching team

– SRS Task force

20

References • Anderson, L. W., Krathwohl, D. R., & Bloom, B. S. (2001). A taxonomy for learning, teaching, and assessing:

A revision of Bloom's taxonomy of educational objectives. Allyn & Bacon. • Ausubel, D. P. (2012). The acquisition and retention of knowledge: A cognitive view. Springer Science &

Business Media. • Blasco-Arcas L., Buil I., Hernandez-Ortega B., & Sese F.J. (2013). Using clickers in class. The role of

interactivity, active collaborative learning and engagement in learning performance. Computer & Education, 62, 102 - 110.

• Chong, A., C. Y., Lam, S. C., Yau, S. W. L., Tam, C. W., & Ho, J. K. M. (2016). Validation of 4-item questionnaire for assessing student engagement on innovative teaching methods. In the International Conference on Open and Flexible Education 2016 (in press). Hong Kong: The Open University of Hong Kong.

• Dangel, H. L., & Wang, C. X. (2008). Student response systems in higher education: Moving beyond linear teaching and surface learning. Journal of Educational Technology Development and Exchange, 1(1), 93-104.

• Du-Babcock, B. (2002). Teaching a large class in Hong Kong. Business Communication Quarterly, 65(1), 80-88.

• Gajjar, S., Sharma, R., Kumar, P., & Rana, M. (2014). Item and test analysis to identify quality multiple choice questions (MCQS) from an assessment of medical students of Ahmedabad, Gujarat. Indian journal of community medicine: official publication of Indian Association of Preventive & Social Medicine, 39(1), 17.

• Hoffman C. & Goodwin S. (2006). A clicker for your thoughts: technology for active learning. New Library World 107, 422 - 433.

• Jones M.E., Antonenko P.D., & Greenwood C.M. (2006). The impact of collaborative and individualized student response system strategies on learner motivation, metacognition, and knowledge transfer. Journal of Computer Assisted Learning, 28(5), 477 - 487.

• Lee, H. S., Liu, O. L., & Linn, M. C. (2011). Validating measurement of knowledge integration in science using multiple-choice and explanation items.Applied Measurement in Education, 24(2), 115-136.

• Yung, K. W. H. (2015). Learning English in the Shadows: Understanding Chinese Learners' Experiences of Private Tutoring. TESOL Quarterly, 49(4), 707-732.

21

An Interactive Learning System for Improving Student's - Hong ...

Documents

Transcript of An Interactive Learning System for Improving Student's - Hong ...