1. Department of Education, Schools Division of Paranaque City, Philippines.

*             Correspondence: lgpantao@gmail.com

Keywords: stoichiometric problem-solving, performance, cooperative learning

1. INTRODUCTION

A compound is created when atoms of various elements react in the proper proportion and quantity, just as a delicious dish is created by adding the exact amount of ingredients. Stoichiometry is a branch of chemistry that deals with the measurement of quantities and mass ratio of elements involved in a chemical reaction [1]. Students study molecular mass, moles, and number of molecules in stoichiometry. They gain knowledge of how to balance chemical equations and use a gravimetric analysis to determine the compound. With so many applications in everyday life, stoichiometry is regarded as the core of chemistry. Stoichiometric calculations are used to determine the chemical composition of every chemical product we use on a daily basis, including shampoos, cleaners, perfumes, soaps, and fertilizers. Stoichiometry is necessary for the chemical industry to function [2]. One of the crucial subjects that is typically covered in introductory chemistry courses is stoichiometry. Students, however, think it is a little difficult. According to [3], there are three reasons why students find it difficult to comprehend. First, balancing equations is hard. Balancing chemical equations is one of the most difficult tasks for students to complete. Due to the numerous steps and the fact that most students do not pay close enough attention, this can be challenging.  However, if one can simplify it for students and break down the steps, they will probably succeed. Second, molar calculations are tough. Calculating molar masses is another concept that students find challenging. They need to be familiar with the molar masses of the elements in order to solve them [4]. They have a hard time remembering them and using them in the right formulas. Third, the students do not see the big picture. This is because they do not comprehend how the various ideas relate to one another or why they are actually living in the real world. Students will likely take more interest and feel more motivated if they can relate what they are learning to real-world situations. Team-based learning (TBL) is a structured approach to small-group instruction that places an emphasis on students’ outside-of-class preparation and in-class application of knowledge [5]. Teams of 5-7 students from various backgrounds are strategically formed from among the students to work together during class.  Students read in advance of class in order to get ready for each unit or module of the course [6].  Known as “modules,” preparation, in-class readiness assurance testing, and application-focused exercise make up the three steps of the teaching method known as “team-based learning,” which is based on scientific research. Typically, a class has one module. Before a class or the start of the module, students must finish the required readings. Materials may be text-based, visual, or in other forms, and they should be pitched at a level appropriate for the class and the students [7]. In the first class of the module, students participate in a “Readiness Assurance Process,” or RAP. Students first take the “Individual Readiness Assurance Test,” or iRAT, on their own, followed by the “Group Readiness Assurance Test,” or gRAT, which they take in groups. The students’ grades are comprised of both their individual and group grades. The tests are frequently multiple choice, and frequently, students take the group test using a “scratch-off” sheet and score it themselves [8]. This practice shortens the grading process and encourages student discussion of the right answers. Individual readiness assurance testing (IRAT) is completed by students and consists of five to twenty multiple-choice questions.  Following the submission of their individual responses, they take the same test, the team RAT (TRAT), together [9].  They use scratch-off cards (IF-AT cards) as a team, looking for a star that denotes a correct response.  Each team’s TRAT score is the same for every member, and both the IRAT and TRAT results are used to determine the students’ grades [10]. Teams can submit a written appeal for an MCQ they believe was written poorly, the answer was incorrectly coded, or their answer choice is preferable. The teacher encourages teams to challenge questions they answered incorrectly after the class has finished the group test. Students are encouraged to review the material, assess their comprehension, and defend their decision as a result of the appeals process [11]. If students feel there is still something wrong with the RAT material, the instructor may go over it again. Exercises that teach students how to apply and extend the knowledge they have already learned and tested make up the majority of the session or module. Teams are presented with a relevant problem or challenge, and they have to come to an agreement on the “best” solution from the available options [12]. Teams then present their selected response, and the teacher leads a class discussion where teams discuss the issue and potential solutions. To conclude the Readiness Assurance Process, the teacher gives a mini-lecture that focuses on concepts with which students struggled the most [13]. The teams must decide how to approach a significant issue in these application activities. Importantly, all teams tackle the same issue and simultaneously report their findings. With this structure, teams are forced to explain their thinking and are given the chance to assess their own justifications in light of potential decisions from other teams. In order to hold students accountable to their teammates, peer evaluation is a crucial component of team-based learning. Team-Based Learning implementation is based on four underlying principles [14]: Groups should be properly formed (e.g. Intellectual talent should be equally distributed among the groups). These teams are fixed for the whole course. Students are accountable for their pre-learning and for working in teams. Team assignments must promote both learning and team development. Students must receive frequent and immediate feedback. This study documented the effect of Team-Based Learning (TBL), as a cooperative learning strategy, to the Stoichiometric problem-solving performance of Paranaque National High School-Main Grade 11 STEM learners in the school.

2. MATERIALS AND METHODS

The study employed a quantitative-descriptive method since the objectives of the researcher is to determine the average result in the problem-solving test before and after the intervention, and the significant increase in the percentage of the Stoichiometric problem-solving performance between the pre-test and post-test results. The participants that will be chosen are the Grade 11 STEM students of Paranaque National High School – Main in Paranaque City taking up General Chemistry 1 subject. Two sections will be used, one will serve as treatment group (Grade 11 STEM Caro) and the other section is the control group (Grade 11 STEMAlcala).

Table 01: The Participants of the Research

Strand	Number of Participants
	Pretest	Posttest
Grade 11 STEM Alcala	48	48
Grade 11 STEM Caro	48	48

The first measurement would serve as the pretest, the second as the posttest. The measurements of observations were collected at the same time for both sections. A diagram of this design is as follows:

A 10-item pretest was administered to both the control and treatment groups covering the topic on this competency in General Chemistry 1: Construct mole or mass ratios for a reaction in order to calculate the amount of reactant needed or amount of product formed in terms of moles or mass (STEM_GC11MRIg-h-38). After administering the pretest, the researcher proceeded to deliver the lesson to both groups. The treatment group was subjected to Team-based Learning and the control group proceeded with solving the stoichiometric problems using the lecture method. After delivery of the lesson, a 10-item posttest was administered to both the control and treatment groups.

Figure 01: The structure of a team-based learning module.

Figure 02: The Flow Chart of conducting the action research.

The proponent wrote a letter of permission to the school asking permission to commence the action research within the classroom premises. This is to highlight ethical considerations that would ensure the well-being and confidentiality of the participants. The written letter served as an expression of the researcher’s commitment to responsible and ethical investigation. The study proactively engaged with colleagues in the validation process of a 10-item pretest ahead of the commencement of the study. With constructive feedback, the study aimed to enhance the tool’s reliability and ensure its alignment with the study’s objectives.

3. RESULTS AND DISCUSSION

The scores from the pretest and posttest served as the data for analysis. The scores were encoded in Microsoft Excel application and transferred to Statistical Package for the Social Sciences (SPSS) for analysis and interpretation. The statistical test used is independent sample t-test. This statistical test is used to compare the mean scores of two different groups of people or conditions. Upon the administration and after the conduct of the examination, the collected data and the result of the pretest and posttest in the first and second trial runs were evaluated and analyzed. Table 02 shows the level of the Stoichiometric problem-solving performance of Grade 11 STEM Alcala learners (Control Group) in the 10-item pretest. In the pretest of the control group, out 48 STEM learners, there is 1 or 2.08% learners who belong to score range from 7-8 and that 1 learner belong to a Very Good level of stoichiometric problem-solving performance. However, there are 8 or 16.67% learners who belong to good level of stoichiometric problem-solving performance; 28 or 58.33% learners belonging to Poor level; and 11 or 22.92% belonging to Very Poor level of stoichiometric problem-solving performance. The pretest mean of the learners of the control group is placed at 3.35 which means that generally, the learners have a Poor level stoichiometric problem-solving performance [15].

Table 02: Level of the Stoichiometric Problem-Solving Performance of Grade 11 STEM Alcala Learners (Control Group) in the Pretest

Score Range (10-item test)	Pretest Frequency	Percent	Level of the Stoichiometric Problem-Solving Performance
9-10	0	0	Excellent
7-8	1	2.08	Very Good
5-6	8	16.67	Good
3-4	28	58.33	Poor
0-2	11	22.92	Very Poor
Total	48	100.00
MEAN	3.35		Poor

Table 03 presents the level of the Stoichiometric problem-solving performance of Grade 11 STEM Caro learners (Treatment Group) in the 10-item pretest. In the pretest of the treatment group, out 48 STEM learners, there are 4 or 8.33% learners who belong to score range from 7-8 and that 4 learners belong to a Very Good level of stoichiometric problem-solving performance. However, there are 13 or 27.08% learners who belong to good level of stoichiometric problem-solving performance; 18 or 37.5% learners belonging to Poor level; and 13 or 27.08% belonging to Very Poor level of stoichiometric problem-solving performance. The pretest mean of the learners of the treatment group is placed at 3.94 which means that generally, the learners have a Poor level stoichiometric problem-solving performance [16].

Table 03: Level of the Stoichiometric Problem-Solving Performance of Grade 11 STEM Caro Learners (Treatment Group) in the Pretest

Score Range (10-item test)	Pretest Frequency	Percent	Level of the Stoichiometric Problem-Solving Performance
9-10	0	0	Excellent
7-8	4	8.33	Very Good
5-6	13	27.08	Good
3-4	18	37.5	Poor
0-2	13	27.08	Very Poor
Total	48	100.00
MEAN	3.94		Poor

Table 04 shows the level of stoichiometric problem-solving performance of Grade 11 STEM Alcala learners (Control Group) and Grade 11 STEM Caro learners (Treatment Group) based on their results in the posttest. In the posttest of the lecture method or the control group, out of 48 STEM learners, there are 4 or 8.33% learners who belong to Very Good level of stoichiometric problem-solving performance. However, there are 23 or 47.92% of the learners who belong to good level of stoichiometric problem-solving performance; 13 or 27.08% of the learners belong to Poor level, and 8 or 16.67% of the learners belong to Very Poor level of stoichiometric problem-solving performance. The posttest mean of the learners is placed at 4.60 which means that generally, the learners have a Poor level stoichiometric problem-solving performance in the control group [17]. On the other hand, the posttest of the team-based learning or the treatment group, out of 48 STEM learners, there are 6 or 12.5% learners who belong to Excellent level of stoichiometric problem-solving performance, and 19 or 39.58% learners who belong to Very Good level of stoichiometric problem-solving performance. However, there are 9 or 18.75% of the learners who belong to good level of stoichiometric problem-solving performance; 10 or 20.83% of the learners belong to Poor level, and 4 or 8.33% of the learners belong to Very Poor level of stoichiometric problem-solving performance. The posttest mean of the learners is placed at 6.04 which means that generally, the learners have a good level stoichiometric problem-solving performance in the treatment group [18].

Table 04: Level of the Stoichiometric Problem-Solving Performance of Grade 11 STEM Alcala Learners (Control Group) and Grade 11 STEM Caro Learners (Treatment Group) in the Posttest

Score Range (10-item test)	Posttest (Control Group) Frequency	Percent	Posttest (Treatment Group) Frequency	Percent
9-10	0	0	6	12.5
7-8	4	8.33	19	39.58
5-6	23	47.92	9	18.75
3-4	13	27.08	10	20.83
0-2	8	16.67	4	8.33
Total	48	100.00	48	100.00
MEAN	4.60		6.04
Interpretation	Poor		Good

Legend:

Range	Interpretation
9.00-10.00	Excellent
7.00-8.99	Very Good
5.00-6.99	Good
3.00-4.99	Poor
0.00-2.99	Very Poor

Table 05 shows the test of significant difference in the level of stoichiometric problem-solving performance in the pretest between the control and the treatment groups. An independent-samples t-test was conducted to determine whether there is a difference in the level of stoichiometric problem-solving performance in the pretest between the control and the treatment groups. The results indicate a not significant difference between control group (M=3.35, SD=1.422) and treatment group (M=3.94, SD=1.643), [p = .066 > .05]. The 95% confidence interval of the difference between means did not indicate a difference between the means of the sample. Consequently, the null hypothesis that there is no difference between the sample means is accepted [19].

Table 05: Test of Significant Difference in the Level of Stoichiometric Problem-Solving Performance in the Pretest Between the Control and the Treatment Groups

Test	Sig.	Interpretation
Pretest	.066	Not Significant

Table 06 shows the test of significant difference in the level of stoichiometric problem-solving performance in the posttest between the control and the treatment groups. An independent-samples t-test was conducted to determine whether there is a difference in the level of stoichiometric problem-solving performance in the posttest between the control and the treatment groups. The results indicate a significant difference between control group (M=4.06, SD=1.747) and treatment group (M=6.04, SD=2.183), [p = .001 < .05]. The 95% confidence interval of the difference between means indicate a difference between the means of the sample. Consequently, the null hypothesis that there is no difference between the sample means is rejected [20].

Table 06: Test of Significant Difference in the Level of Stoichiometric Problem-Solving Performance in the Posttest Between the Control and the Treatment Groups

Test	Sig.	Interpretation
Posttest	.001	Significant

 

Table 07: Action Research Work Plan and Time Frame

Objectives	Activities/Strategies	Persons Involved	Time Frame
To disseminate the results of the action research	Submit the result of the action research to the School Head	Proponent and School Head	2025
To disseminate the results of the action research	Present the outcome and intervention to the Senior High School (SHS) science teachers	Proponent and SHS Science Teachers	2025
To utilize the results of the action research	Use the findings in addressing the problems in teaching other subject areas	Proponent and SHS Science Teachers	2025
To utilize the results of the action research	Conduct an action research similar intervention to address a specific problem in other subject areas	Proponent and SHS Science Teachers	2025

 

4. CONCLUSION

Stoichiometry is a vital component of introductory chemistry courses, and it’s often a challenging topic for STEM learners to grasp. As a foundational concept, it sets the stage for more advanced topics, making it essential for students to understand. To address this challenge, researchers explored the effectiveness of Team-Based Learning (TBL), a structured approach to small-group instruction. In TBL, students work in teams of 5-7, strategically formed to bring together diverse backgrounds and perspectives. This approach emphasizes outside-of-class preparation and in-class application of knowledge, allowing students to work collaboratively to solve problems. The study focused on Grade 11 STEM learners at Paranaque National High School-Main, using a quantitative-descriptive method to investigate the impact of TBL on stoichiometric problem-solving performance. The results revealed a significant difference between the control group and the treatment group, with the treatment group showing a marked improvement in performance. Specifically, the control group had a mean score of 4.06 with a standard deviation of 1.747, while the treatment group had a mean score of 6.04 with a standard deviation of 2.183. This difference was statistically significant, with a p-value of 0.001, indicating that the results are unlikely to be due to chance. These findings suggest that TBL can be an effective strategy for enhancing students’ understanding of stoichiometry and improving their problem-solving skills. It is proposed that teachers adopt Team-Based Learning (TBL) to improve students’ stoichiometric problem-solving performance. With careful planning, implementation, and evaluation, most students can achieve success through this approach. The effectiveness of TBL can be attributed to several key factors. According to Carleton College, TBL’s success lies in its ability to actively engage students in the learning process. By allocating most of the in-person course time to Application Exercises, students work in groups to tackle challenging problems, promoting collaboration and a shared understanding of complex concepts. TBL’s structured framework sets it apart from other teaching methods. It combines peer evaluation with a series of planned cycles, including readiness assurance and application activities, which are completed by specially chosen permanent teams. This integrated approach gives TBL its power, as it’s more than just a set of methods or exercises. By providing teams with significant, challenging problems, TBL encourages high-performing team development, requiring students to apply course principles and concepts in new ways. Furthermore, TBL holds students accountable for their pre-class learning through individual Readiness Assurance Tests (iRATs). This accountability is reinforced through team Readiness Assurance Tests (tRATs) and Application Exercises, which encourage students to work together and rely on one another. By promoting active learning, collaboration, and accountability, TBL creates an environment where students can develop their problem-solving skills and achieve success in stoichiometry. Peer assessments, which are both formative and summative and ask students to rate the contribution of their teammates to the team’s performance in the classroom, provide an additional layer of accountability; and to ensure that students are prepared for increasingly complex applications of course concepts, TBL scaffolds the learning process by having them go through a “readiness assurance process” for each topic (or module) in the course. Team-Based Learning (TBL) is a cooperative learning method that has shown promise in boosting learners’ mean performance. In a study, learners who participated in TBL (the treatment group) displayed greater enthusiasm for the lesson, particularly during group tests where they received immediate feedback by scratching an answer sheet. This interactive approach enables learners to apply what they’ve learned and refine their problem-solving skills through repeated self-evaluations and revisions in a collaborative environment. Furthermore, TBL provides students with immediate feedback on their quiz scores, which enhances learning objectives and promotes a more effective learning experience. By leveraging the benefits of teamwork and instant feedback, TBL can be a valuable tool for educators seeking to improve student outcomes.

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