Gaining and Maintaining Student Attention Through Competitive Activities in Cooperative Learning

A well-received experience in an undergraduate introductory Artificial Intelligence course

Piyanuch Silapachote and Ananta Srisuphab Faculty of Information and Communication Technology, Mahidol University

Nakhon Pathom, Thailand {piyanuch.sil, ananta.sri}@mahidol.ac.th

Abstract— Digital-age undergraduate students exhibit a very short attention span, which is rapidly diminishing even further, posing a critical concern to today’s academic community. This presents an immediate need for a non-traditional teaching and learning approach that is well-adapted to their diverse modern life-style. Instead of lectures only, interactive in-class activities do attract some attention, but do not guarantee to retain this focus. Maintaining students’ attention, keeping them concentrated while they are studying, is no less of a crucial dilemma. It is therefore the focus of this research. Working in small groups is engaging and fun but students may easily move off-topic when a task is slightly complex or demanding. To overcome this, we are incorporating challenging elements, featuring built-in competitiveness into every cooperative activity and exercise we organize in our classroom, an introduction to Artificial Intelligence (AI). Exploiting the strong competitive nature of college students allows us to securely capture their full attention. Our students continue enjoying the activities, collectively focusing their efforts on given tasks, and undertaking assigned projects. As a consequence, and often without realizing it, they are learning new concepts first-hand with real examples. To evaluate the effectiveness of our methodology, students completed a questionnaire with targeted questions. Their feedback provides a strong positive validation of the approach taken.

Keywords— short attention span; competitive classroom activity; cooperative learning; co-teaching

I. INTRODUCTION A major challenge in teaching undergraduates today is not

only how to gain students’ attention but also how to maintain it. Short to extremely short attention span is a global phenomenon in academia, particularly for those born into the age of digital technologies, fostering a multitasking trait [1]. Various digital media is a primary source of extensive and constant distractions. Many are not able to resist the urge to check and to respond to instant messages on their very active social media channels.

A role and a responsibility of every instructor is to facilitate and to strengthen student engagement in learning, however that is a very delicate process. Aggressive enforcement is evidently turning students against instead of toward studying. Gradual persuasion, tender encouragement, or instinctively drawing their attention is most effective. A common approach is to organize classroom activities in place of or in combination with a lecture base model. As it turns out, not every activity is created equally.

Choices of activities and how they are implemented significantly affect their effectiveness. Our experience with an undergraduate introductory AI course demonstrates that adding elements of competitiveness plays a vital role in the success of classroom activities especially when coupled with cooperative learning.

The paper first outlines the challenges involved in teaching AI, followed by a description of our approach, and concludes with a discussion of the results of student’s evaluations.

II. CHALLENGES IN TEACHING AN INTRODUCTOION TO AI AI is commonly a required course in many undergraduate

programs in Computer Science, Computer Engineering, as well as Information and Communication Technology. Recognized as a challenging subject, AI requires not only a strong background in mathematics and discrete structures, but also problem solving skills, logical reasoning, and analytical thinking. Foundations of computer science are essential prerequisites, particularly data structures and algorithms. Programming skills, both imperative and declarative (functional and logic), are essential for students to deepen their comprehension of AI. Because of a generally high degree of abstraction of AI algorithms, students often perceive a lack of connections to practical applications.

Attempting to make AI easier and to persuade more students into this field, researchers have often made use of a strong link between AI and games. The University of Southern California developed a Bachelor’s program in Computer Science equipped with game design and engineering. Introduction to AI was part of this curriculum and efforts were put into teaching it with games [2]. To help establish mental models of the AI algorithms, students developed intelligent controllers for a simulated vehicle using an animated toolkit. This two-stage assignment ended with competitive battles, reportedly creating an enjoyable learning experience [3]. Similarly, in order to motivate students, a project concluding with a competitive tournament was integrated into a weekly 1-hour lab alongside a 3.5-hour lecture. Ataxx, a zero-sum, perfect information game in Prolog was employed [4].

The aforementioned approaches to teaching undergraduate AI involve creative components, notably competitive games, to facilitate students’ learning. However, they concentrate only on enriching programming assignments. In contrast, we propose a model to enhance student engagement in learning AI starting in a classroom by appealing to their natural competitiveness.

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Tailoring to the multitasking talent and targeting the very short attention span encountered worldwide in today’s typical college students, neither old-fashioned chalk and blackboard nor conventional whiteboard and projector classrooms are adequate. Lectures are generally passively monotonous, causing boredom and drowsiness. Occasional active question and answer sessions increase class participation but often reach only the already self-motivated students. Promoting full engagement of every student greatly benefits from well-structured cooperative activities [5].

Teaching and learning of difficult computer science subject, including AI, could benefit from an activity-based approach. We conjecture that this would be appropriate even for a theoretical course on formal languages and automata [6].

III. CONTENTS OF THE FIRST PART OF OUR AI COURSE Fundamentals of AI typically start with problem solving via

search that involves modeling real world problems, representing states, specifying operators, defining search spaces, constructing search trees, and applying search algorithms. A large percentage of students struggle to understand this seemingly simple topic. Formulating state representations and corresponding operators requires substantial problem solving skills to logically analyze problems step-by-step. Recognizing ones that are concise and easy to implement entails art and creativity. Practice is the key to mastery as teaching of concepts can only go so far. Similarly, designing powerful heuristics, integral components of informed search, relies on effective virtual imagination, abstractly forming mental images of a flow of states and how operators interplay.

IV. LOCKING STUDENTS’ ATTENTION BY FEATURING COMPETITIVE ELEMENTS IN EVERY COOPERATIVE ACTIVITY

Lecturing on search may not provide the necessary hands-on practice, attract students, or extend their attention spanning time. Employing ordinary activity-based learning engages students, but may not be adequate to keep them focused. Many quickly lose interest, especially if they do not immediately succeed. A high level of abstract knowledge contributes to discouragement. Many students have a tendency to sidestep logical thinking and analysis. It is a challenge of instructors to guide them across this barrier and to draw out their full potentials. Constant intellectual stimulations are no longer an accessory but a necessity.

Our approach is to persuade students through their natural competitiveness built into every small-group activity. Unlocking excitement and enjoyment, competitive features keep students motivated, automatically stimulate, and continuously energize them. Group competitions bring about students’ enthusiasm and their commitment to the team. With full attention, delivery of concepts becomes effective, as students are willingly learning.

Another aspect of our teaching scheme aims at repetitions of related tasks. Ideas are reinforced through series of examples with different levels of complexity, which in many cases allow for a programming implementation to aid their understanding and to illustrate applications of knowledge to practical scenarios.

V. DESIGN AND ORGANIZATION OF OUR CLASS ACTIVITIES Activities employed in our AI class include a mix of classical

and newly introduced problems, adapted and modified with an integration of competitive elements for every task.

A. State Representations, State Space, and Search Trees We begin the first lesson with a game of Nim. Most students

start playing without giving it much consideration. After a few trials, intensity grows as a desire to win naturally kicks in and students uncover a magic number necessary for strategizing vital moves at the endgames. Only a small group of students are able to extend their reasoning to perceive a full tactic that guarantees a win regardless of where they are in the game. Instead of pressuring every student to completely solve the entire game, we hold on to the positive atmosphere and speedily move to state representations and search trees. With Nim’s small search space, its complete game tree can be hand-drawn on a single page clearly presenting multiple paths from initial to goal states.

B. Uninformed or Blind Search Methods The second activity is to find a path in a maze, a very simple

task that does not result in getting much attention. It is, however, an important AI problem to explore. Inspired by real corn mazes we turn it into a fun competition to find a path without seeing the maze. A group of six students divides into two teams of three. Each holds a secret maze for the other team to explore. The team to find a path first wins the match. While navigating, the only information provided is where they can go, i.e. the four cardinal directions, from a square they inquire about. Shortly afterwards, students realize that they need a strategy on how to track where they have been and where to go. We observe how activities play out and wait until most teams hit at least one dead-end, where finding a way to backtrack is needed. This exercise links directly to a revision of state representation and search. Students draw up their maze enabling them to track their moves.

Modeling of a maze is essentially the first problem solved solely by team effort. Many solutions may neither be complete nor well formulated but decently demonstrate a learning process: a task is analyzed and a search tree is drawn. Applying search techniques follow spontaneously. Without having been formally introduced and without being aware of it, teams employ breadth-first or depth-first search. At the conclusion of the session, the details are then described. Letting students work on a task and achieve a goal before revealing the underlying theories carries certain benefits. It eases understanding since students have already practiced it firsthand. It assures that applying knowledge to practice, though seemingly difficult with substantial logical requirements and a sizable-level of abstraction, is not beyond the students’ capability, proven by the task they have just finished.

C. Informed Search Techniques and Heuristics Keeping the momentum going, the third lesson involves a

classic n-queen problem. Students enjoy an initial challenge: how many unique solutions they can find for 8 queens. Every group quickly gathers up ideas, attempting to be the first team to find a valid solution and later the team that finds the maximum number of unique solutions. Some try to generate one solution from another, while others uncover each of them individually.

Next, we ask every group to design a state representation and to draw a corresponding search tree. By working on it hands-on, students are able to discover our hidden agenda and confidently express that a complete search tree is too complicate to write down. We take this opportunity to remind them what they have learned so far: from a simple game of Nim where they can easily

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draw an entire search tree, to a maze, and now a problem of n-queen where there could be billions of nodes. Regardless of the complexities, every problem requires good state representations, which they now have seen in a set of representative examples.

At this stage, we introduce heuristic functions and explain their role, advancing from blind to informed search. A heuristic is a relatively new concept for most students and a challenging one for many. Instead of immediately presenting a mathematical formula, we urge them to explore small branches of their tree: letting them explain which node they wish to expand and why. With many possible solutions involving elementary-level logic, students promptly participate in a class-wide discussion, voting for their preferred node. We turn it into a heuristic function and essentially expose how AI mimics what humans do.

Reinforcing the comprehension of heuristic functions and informed search techniques, we pass around small fifteen-puzzle board games. Bearing just enough of a challenge and at the same time not being too complicated, the puzzle energizes a classroom triggering active collaborations and maintaining the attention. Making use of a highly enthusiastic atmosphere, we ask students to formulate a search algorithm using a heuristic of their choice.

D. Minimax Game Trees and the Alpha-Beta Pruning Routine Battles of a tic-tac-toe game provide another comprehensive

exercise. Making use of the knowledge to solve problems via search, each group formulates a state representation, defines a search space, selects a suitable search algorithm, and applies it. Instead of guiding the class step-by-step, students work on their own while we monitor and assist the progress of every group.

The tic-tac-toe activity promptly connects students to the last topic on search: using minimax trees for alternate moves in two-player games, followed by alpha-beta pruning algorithms.

VI. OUR CLASSROOMS According the undergraduate curriculum at our faculty, the

Faculty of Information and Communication Technology (ICT) at Mahidol University in Thailand, this introductory AI course is required for every third year student. Both authors are the sole instructors for this course during the first semester of the 2013 academic year (August through November 2013). The activities proposed here are conducted during the first half of the course, i.e. before the midterm examination. Specifically for this group of students, AI is their only class at the Faculty of ICT that fully engages them in a cooperative learning environment. AI is also their only class that employs a co-teaching model.

There are a total of 202 registered students, divided into three separate sections. We use a co-teaching model [7] and hence all three sections are co-taught by both instructors. Consistency of lecture contents, additional materials, and cooperative activities is strictly maintained, while dynamics across the three sections are managed through various approaches of co-teaching. Most lectures employ a team-teaching model, while some utilize one-teach-one-observe or one-teach-one-assist. Students form their own groups of three when working on cooperative activities. We carefully monitor every group in isolation, considering not only academic performance but also social behavior. Different needs of each student are individualized and addressed by their peers and by the instructors as discussions are strongly encouraged.

VII. STUDENT EVALUATIONS AND FEEDBACKS At the beginning of an in-class review session approximately

one week before the midterm exam, we asked our students to fill out a questionnaire evaluating our teaching model and targeting their learning experience. Students were made aware that their invaluable feedback would be used exclusively for educational research purposes and to improve the quality of the AI class in coming years. We informed the students that their responses on the questionnaire would have no effect on their grade. Each feedback is anonymous and kept confidential. Furthermore, the questionnaire was optional, and 172 of the students voluntarily responded. Evaluations and interpretations of the questionnaire can be divided into three components: attention spanning time, teaching methodology, and general comments. Corresponding findings of each are summarized in order below.

A. Effects on Attention Spanning Time A student is to estimate his or her attention span (in minutes)

during a typical lecture compared to a classroom activity period. Most computer science classes at the Faculty of ICT, Mahidol University, are three hours long with a half-time break of ten to fifteen minutes. To assist estimation, each student had to choose one of the ten blocks of time that we discretized based on a three-hour class-time. The blocks are unevenly divided putting more emphasis on the shorter end (details are shown on the horizontal axis of the histogram in Fig. 1). Comparing the two distributions plotted in Fig. 1, it is apparent that concentration of our students is significantly longer during activities, all of which incorporate competitive elements, than during lectures. Specifically, 73.26% of our students report being able to concentrate longer during the activities compared to lectures, 18.02% report no differences between the two approaches, and the remaining 8.72% can focus better during lecture periods.

Organizing structured activities in a classroom effectively extends attention span by an average of 50.79 minutes (of those who report longer attention spanning time compared to lectures). This is a significant result accounting for one-third of a regular 3-hour-long class. The top 4% (7 of 172 responses) indicate over 120 minutes extended attention spanning time, i.e. concentrating longer for over two-thirds of the entire class-time. Comments by these seven students include expressions of a particularly strong liking towards cooperative activities. They value learning by doing with a group of friends, through which they gain more knowledge. Simultaneously, they state that this is not a boring class. Playing games (activities with competitive elements we have implemented largely involve logical puzzles and games), discussing about the strategies, facilitate their learning. They can understand the difficult lessons easier.

B. Feedback on our Teaching Methodology To assess how our approach to teaching an undergraduate

introductory AI course, in a governing competitive environment, reflects on students’ learning perspectives, we asked them to rate how much they agree with the following three aspects:

Q1: Learning by doing. Students work on an assigned task hands-on. They first solve a problem applying their background knowledge together with materials covered earlier in the course. New concepts are introduced as activities move along. Then, they are connected to the underlying AI theories.

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Q2: Cooperative learning. In a small group of three, students collectively work together to accomplish a goal. Depending on one another’s skills, they openly discuss their ideas, supporting, defending, or arguing their similarities or differences.

Q3: Repetitive exercises. Concepts are repeatedly reinforced by adopting similar activities with increasing complexities. Important concepts such as representation and modeling of real-world problems are regularly revisited with variety of examples.

The majority of the students in our class agree with all three aspects, marginally less desire repetitive exercises. Histograms of the students’ responses are presented in Fig. 2. Using a rubric in Table 1 for a quantitative evaluation of our teaching methods, the score for learning by doing is 4.03 (out of 5), cooperative learning 3.97, and repetitive exercises 3.76.

A few students who disagree or strongly disagree with either learning by doing or with cooperative learning commented on the class-sizes, preferring fewer students per class for activity-based learning so as to decrease student-instructor ratio and, as a consequence, to increase student-instructor interactions. They further explained that they would like more diverse groups, mixing among those who are good at the subject and those who tend to struggle. This way, they could get help from friends and would not be falling behind. At the same time, this could reduce a gap between groups as the ones where all members are good tend to finish the tasks much quicker than others.

Carefully considering their comments, we believe that these students are not truly against learning by doing or cooperative learning. However, they appear uncomfortable working actively in a large crowd, feeling academically inferior to their peers. This could likely be reconciled, even unnoticeably to students, by switching to different co-teaching models at some times. Notably applicable are station-teaching and alternate-teaching.

C. Observations and General Comments Observing the classes while teaching them, it is clear to us

that competitive activities do lead to more engagements. Some students stay so focused; they continue discussing or working on the task even during an always much anticipated half-time break. In a few instances, over 75% of the class skipped the break.

Students remark positively on their first-time active learning experience in this AI course. Group discussion with friends is very enjoyable. Activities keep them awake. They realize that playing games competitively is not only fun, but could also be educational. Concepts become easier to understand even though connecting to the underlying theories is sometimes challenging. Many appreciate the opportunities to practice logical thinking.

VIII. CONCLUSION AND FUTURE WORK Embedding competitive elements into cooperative activities

significantly lengthen students’ short attention span. Supported and confirmed by students taking a challenging, highly abstract introductory to AI, this teaching methodology is well-received. It shows high potential to be applied in other engineering courses.

REFERENCES [1] K. Purcell and et al., How teens do research in the digital world, Pew

Research Center’s Internet & American Life Project, Nov, 2012. [2] M. Zyda and S. Koenig. Teaching artificial intelligence playfully.

Proceedings of the AAAI-08 Education Colloquium, 90-95, 2008. [3] P. Hingston, B. Combes, and M. Masek. Teaching an undergraduate AI

course with games and simulation. Proc. 1st Intl. Conf. on Technologies for E-Learning and Digital Entertainment (Edutainment), 494-506, 2006.

[4] P. Ribeiro, H. Simões, and M. Ferreira, Teaching artificial intelligence and logic programming in a competitive environment, Informatics in Education, vol. 8, no. 1, pp. 85-100, January 2009.

[5] R. Killen, Effective teaching strategies: lessons from research and practice, 4th ed. South Melbourne, Vic.: Thomson Social Science, 2007.

[6] M. Vijayalaskhmi and K.G. Karibasappa, Activity based teaching learning in formal languages and automata theory - An experience, IEEE Engineering Education: Innovative Practices and Future Trends, 2012.

[7] M. P. Friend and L. Cook, Interactions: Collaboration skills for school professionals, 6th ed. Upper Saddle River, NJ: Pearson, 2010.

TABLE I. A RUBRIC FOR EVALATING OUR TEACHING METHODOLOGIES

Strongly agree Agree Neutral Disagree Strongly disagree 5 4 3 2 1

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Blocks of attention spanning time (in minutes)

Students' Self-Estimated Attention SpansLectures Activities

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Learning by doing Cooperative learning Repetitive exercises

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Student Feedback on our Teaching Approaches

Strongly agree Agree Neutral Disagree Strongly disagree

Fig. 2. Histogram reflecting the opinions of our students in regards to their new classroom experience with respect to our teaching methodology. The three aspects being evaluated are (1) learning by doing, (2) cooperative learning, and (3) use of repetitive exercises for reinforcement.

Fig. 1. Histogram of students’ self-estimated attention-span time in our introductory AI class. This graph compares student learning experiences in the classroom during which educationally cooperative activities are structurally being organized versus when regular lectures are being delivered.

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