Task Interpretation To Improve The Students’ Learning Outcomes Of Family Welfare Science Course

Luthfiyah Nurlaela

Ita Fatchur Romadhoni

Abstract: Students' engagement in learning is affected by the learning environment including assigned activities and tasks, ways to provide instructions, and assessment practices. It is implied that lecturers affect students’ interpretation task (for better or worse) in what way they organize their learning.The present study aimed to analyze the influence of students’ task interpretation on their learning outcomes. It was an experimental type of research with pretest and posttest control group design. The research subjects were 64 students of the Dressmaking Study Programof the Home Economics Department of Faculty of Engineering, of UniversitasNegeri Surabaya (UNESA). They were taking Family Welfare Studies course. They were divided into an experimental class (29 students) and control group (35 students). The experimental class was given task-analysis sheet prior to pre-test, while the control class was not. Another similar treatment for both groups was guided group discussions with Student Activity Sheet (MFI), and posttest. The technique for data collection was tests.The data analysis used N-Gain and t-test. The result showed that: (1) The mean of N-Gain of the control class was .44 (medium category) and the mean of the experimental class was .73 (high category); (2) the result of t-test showed that with significance level of .05, the obtained value was .019. It means that there was a difference in the learning outcomes of the experimental and the control groups. In conclusion, the students’ task interpretation affects their learning outcomes.

Key words: task interpretation, students’ learning outcomes, Family Welfare Studies course

Introduction

In order to achieve a successful learning, each student must have a consistent approach (work habit) to complete academic tasks (Butler & Cartier, 2004), including the students' ability to interpret the demands of work to be accomplished. Therefore the task interpretation is crucial in determining the learning success. It is necessary to study what a lecturercan do to improve the task interpretation effectively.

In class, lecturers construct learning environments where students work. For instance, they choose instructional methods (e.g. lectures, small group discussions); objectives, forms and components of learning activities; and evaluation practices (which is the standard for evaluating the task). In a learning environment, lecturers have the potential to affect the formation of knowledge and competences of students. The learning environment formsthe students’ approach to learning. This will ultimately affect their learning (Entwistle&Tait, 1995).

When creating a learning environment, a lecturer conceptualizes and arranges academic tasks (academic work). As part of this process, he or she designs activities for students with the goal of fostering a particular academic work habit and learning outcomes.

The term "activity" generally refers to a task assigned by a lecturer. The term "task" refers to a specific and more coherent activity and internally requiring some other learning activities (such as reading, writing, learning, and problem solving).

Students’ interpretation of assigned tasks describes their work habits. It is of importance as a basis of success in accomplishing the task. Some researchers define "engagement" as a very meaningful and thoughtful approach to complete a task (Paris & Paris, 2001). Referring to the model of metacognition and self-regulation (Butler &Winne, 1995; Zimmerman &Schunk, 2001), students engagement is defined as a student activity, coordinating reflective learning process (i.e. self-regulation) in improving the knowledge of metacognitive and beliefs of motivation in the context of academic tasks. Thus, student involvement is associated with self-regulation in the activities, because it lies in the context of learning (Zimmerman &Schunk, 2001). In connection with this definition, engagement in the task can be divided into two recursive phases: task interpretation (carefully defining the requirements of a specific task), planning (setting a destination, selecting approaches to manage tasks), implementation (implementing the chosen strategy), monitoring (continuously tracking the progress associated with the destination), and evaluation (generating feedbacks about things that have already occurred).

Explaining student engagement requires an analysis of the quality of their participation in that phase (Butler &Winne, 1995). It is because students gain experience through tasks, work repeatedly through these phases. By doing so, they begin to develop the work habits which they adopt whenever they are faced with academic work.

Task interpretation is very important for learners’ success in their performance. Butler (1998) stated that "efficient learners are aware of the task requirements and direct their learning activities accordingly”.During the first phase of engagement, namely the taskinterpretation, they interpret the requirements of a given task. Then, they self-regulate all future learning activities based on their interpretation of the demands of the task (Butler &Winne, 1995). The interpretation serves to direct duties (e.g. purpose for which they have set in advance), the strategy they choose and apply, and criteria for the assessment of their performance during the monitoring and self-evaluation.

In other words, if the interpretation of the task is missing or wrong, the learning will not succeed. A student can work diligently and hard, but their efforts would not be productive to focus on the learning objectives in question. Thereforethe task successful interpretation will lead to engagement focused on the tasks and ultimately affect the students’ success.Therefore, to be successful, the students must have an approach to academic work including their attention on the task interpretation.

As previously mentioned, the students' engagement in learning is influenced by the learning environment, including the activities and tasks assigned, how the instructions provided, and assessment practices. It is implied that the lecturer can affect the students’ task interpretationin the way they organize the learning environment.

Family Welfare Studies is a compulsory course for the students majoring in Home Economics Education, including those of Dressmaking Study Program. This course is provided at the beginning of the semester (1st semester). The purpose of this course includes: 1) the students understand the basic concepts of Family Welfare Science and its scope; and 2) the students master the Family Welfare Studies as the basis for the development of the Home Economics Education, as well as mastery of the welfare of the family, family life and their relation to the state life. The course materials are comprised of Family Welfare Science, Home Economics, education, family welfare (PKK), family life as part of science, theoretical perspectives on the family, the nature of the family, management of family resources as a system, the definition and scope of family resources, the concept of decision-making in the family, the allocation of time and housework, family financial management, gender roles, family welfare, and research methods in the family.

In this study, the role of gender was selected as a topic to be studied, because the topic was considered to be quite interesting and constituted many ill-defined problems. Practicing problem solving skills requires those ill-defined problems. The students’ activities of creative and critical thinking can take place. The task analysis, pretest, posttest, and student activity sheets were developed to guide the activities.

The problems of this research raised are: 1) How are the students’ learning outcomes; and 2) Does the students’ task interpretation influence their learning outcomes?

Method

The present study was a quasi-experimental research with a pretest-posttest control group design. The students’ task interpretation was the independent variable and the students’ learning outcomes was the dependent variable. The subjects were the students of Dressmaking Study Program who were taking the Family Welfare Studies course. They were divided into the experimental group (B Class) consisting of 29 students and the control class (A Class) totaling 35 students. The students in the experimental class were given a sheet of task-analysis prior to the pretest, while the control class was not given the task-analysis sheet before the pretest. The similar treatmentsfor both groups were the pretest, guided group discussions with Student Activity Sheet (MFI), and the post-test. To gather the data, tests were used.

The students’ learning outcomes were determined on the basis of the students’ test results consisting of the pretests and posttests. The results of both tests were used to determine the N-gain score which accounted for the level of the students’ understanding. The qualitative descriptive analysis was done to determine the learning outcome (Hake, 1999) with the following formula:

Notes:

<g> = gain score (improvement of the students’ learning outcomes)

S_pos_t = post testscore

S_pre = pre testscore

S_ma_x = maximum score

The N-gain indicates the differences in understanding of the concepts before and after the treatment. The criteria for the N-gain according to Hake (1999) are divided into three levels. They are:

(1) If <g> ≥.7, it is categorized as ‘high gain’.

(2) If 0.7><g> ≥ .3, it is categorized as ‘fair/medium gain’.

(3) If <g> .3, it is categorized as ‘low gain’.

Result and Discussion

The pretest results

Before conducting the study, the researchers administered a pretest to determine the students’ initial ability. The pretest results can be seen in Table 1 as follows.

Table 1 The pretest results

Data	Control Class	Experimental Class
Maximum score	16	26
Minimum score	58	55
Mean	40.74	47.10
Median	44	48
Modus	45.00	48.00
Standard Deviation	10.82	5.63

Based on the pretest results of the students in the control class, the class obtained the highest score of 58, the lowest score of 16, the average score of 40.74 with a standard deviation of 10.82, the median of 44, and the modus of 45.

From the results of the pretest of the experimental class, the scores earned included the highest scoreof 55, the lowest score of 26, the average score of 47.10 with a standard deviation of 5.63, the median of 48 and the modus of 48.

The posttest results

After the implementation of learning activities and the administration ofthe Student Worksheet and tests, the evaluation was performed to determine the students’ learning outcomes in the form of post-test. The posttest results can be seen in Table 2 below.

Table2The posttest results

Data	Control Class	Experimental Class
Maximum score	78	87
Minimum score	53	77
Mean	66.8	85.5
Median	67	86
Modus	62	86
Standard Deviation	6.15	2.11

From Table 2, the results of the posttests showed that the control class obtained the highest score of 78, the lowest score of 53, the average score (mean) of 66.8 with a standard deviation of 2.11, the median of 86, and the modus of 86.

Whereas the posttest results of the experimental class showed that they obtained the highest score of 87, the lowest score of 77, the average score (mean) of 85.5 with a standard deviation of 6.15, the median of 67, and the modus of 62.

The N-Gain scores

The improvement of the students’ learning outcomes in the control class can be seen from the N-gain average score of 0.44 (included in the medium category); whereas the one of the experimental class was 0.73 (included in the high category). Table 3 below reveals the results of N-gain scores.

Table 3 The N-Gain scores

No	Class	N	Scores
No	Class	N	Ideal scores	Minimum Scores	Maximum Scores	Average
1.	Control	35	100	.31	.56	.44
2.	Experimental	29	100	.63	.78	.73

Table 3 reveals that there are 10.34% or 3 students included in the low category, 13.79% or 4 students included in the medium category, and 75.86% or 22 students in the high category. This shows the significant increase in the students’ learning results after having been given the task analysis.

From Table 3, the N-gain scores were included mostly in the category of medium and high. This suggests that there was a significant improvement in the students’ learning outcomes. The task analysis assessed affected the learning outcomes. The increase in task analysis can be seen in Figure 1 below:

Figure 1 the increase of task analysis

The t-test results

The data analysis concerning the students’ learning outcomes by using the t-test is presented in Table 4.

Table 4Independent Samples t-Test

		Levene's Test for Equality of Variances		t-test for Equality of Means
		F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
									Lower	Upper
Values	Equal variances assumed	30.220	.000	-15.583	62	.019	-18.68	1.19927	-21.08598	-16.29135
	Equal variances not assumed			-16.800	43.30	.035	-18.68	1.11239	-20.93156	-16.44578

Table 4 shows that the obtained results of the analysis was the Sig. (2-tailed) of 0.019 <0.05. On the basis for a decision in an independent sample t-test, it can be concluded that H0 was rejected and H1 was accepted. It means that there is influence of the students’ task interpretation on the students’ learning outcomes.

The task analysis is believed to lead the student to understand and resolve the tasks. From time to time and through studies at schools, the researchers describe how the students developedtheir knowledge of the academic context on which they base their approach to the academic work.As a part the students’ built student knowledge, the students develop metacognitive knowledge (i. e. the knowledge of knowledge), that influence their approach to academic tasks.

At the beginning of the definition of metacognition, Flavel (1987) defined the three types of metacognitive knowledge, the person variable, task, and strategy, which affects the students’ approach to academic work. The person variable reflects the students’ knowledge about themselves as learners and others, and about learning the strategies in general. The strategy variable reflectsthe students’ knowledge about how, when, and where the specific learning strategies should be used. The task variable reflects the "students' understanding about relationships between characteristics and associated processing task demands" (Butler, 1998, p. 280). Flavel found that person, strategy, and task variables interact to shape how students are involved in the assigned tasks. The students build metacognitive knowledge all the time, from their continuous experience with academic work (Paris, Byrnes, & Paris, 2001). They juxtapose the metacognitive knowledge when performing the self-regulating in the context of any particular task (Zimmerman &Schunk, 2001).

In order to succeed in learning, the learners must know more than just the purposes of tasks. They also need to understand how the academic tasks are accomplished.

The students’ task interpretation of the students lead their planning, (e. g. purpose for which they created), the strategy they choose and how they are applied, and the criteria for the assessment of their performance during the monitoring and self-evaluation. Thus, if the interpretation of the assignment is missing or incorrect, the learning will fail. A student can work diligently and hard, but their efforts would not be productive to focus on the learning objectives intended. Thus, the interpretation of a successful assignment is the basis for a focused engagement in the tasks and they will finally achieve the successful learning at schools. Therefore, to be successful, the students must adopt an approach to academic work which is usually included their attention to the task interpretation.

Conclusion and Suggestion

Based on the previous data analysis, some conclusions can be drawn as follows: 1) The mean N-Gain of the control class was .44 (medium category), and the mean of the experimental class was .73 (high category); 2) The result of t-test showed that with a significance level of .05, the obtained value was .019, which means that there is a difference between the learning outcomes of the experimental and the control groups. It therefore can be concluded that the students’ task interpretation affects their learning results.

The suggestions that can be raised include the need to apply the task interpretation in giving each task to the students. The task Interpretation helps the students understand the task, plan to complete the task, and ultimately help them achieve the best academic achievements.

References

Abbasian, Gholam-Reza &AnahidHartoonian. (2014). Using Self-Regulated Learning Strategies in Enhancing Language Proficiency with a Focus on Reading Comprehension. English Language Teaching; Vol. 7, No. 6; 2014 ISSN 1916-4742 E-ISSN 1916-4750 Published by Canadian Center of Science and Education, p. 160-167.

Butler, Deborah L. & Sylvie C. Cartier. (2004). Promoting Effective Task Interpretation as an Important Work Habit: A Key to Successful Teaching and Learning. Teachers College Record Volume 106, Number 9, September 2004. pp. 1729-1758. Copyright by Teachers College Columbia University.

Butler, D. L., &Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65, 245-281.

Cartier, Butler & Bouchard (2010). Teachers working together to foster self-regulated learning through reading by students in an elementary school located in a disadvantaged area.Psychological Test and Assessment Modeling, Volume 52, 2010 (4), 382-418.

Cartier, S. C., & Butler, D. L. (2004, May).Elaboration and validation of the questionnaires and plan for analysis. Paper presented at the annual meetings of the Canadian Society for Studies in Education, Toronto, ON, Canada.

Dunn, R., & Dunn, K. (1992). Teaching secondary students through their individual learning styles: Practical approaches for grades 7-12. Boston: Allyn and Bacon.

Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp. 231-236), Hillsdale, NJ: Erlbaum.

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry. American Psychologist, 34, 906–911.

Georghiades, P. (2000). Beyond conceptual change learning in science education: Focusing on transfer, durability and metacognition. Educational Research, 42, 119-139.

Gourgey, A. (1998). Metacognition in basic skills instruction.Instructional Science, 26(1-2), 81-96.

Hadwin, Allyson. (2006). Do your students really understand your assignment? LTC Uvic. Vol. II, No. 3, August 2006, p. 1.

Hadwin, Allyson F, Mika Oshiga, Mariel Miller, Peter Wild. Examining Student and Instructor Task Perception in a Complex Engineering Design Task.

Lawanto, Oenardi, Deborah Butler, Sylvie C. Cartier, Harry B. Santoso, Wade Goodrige, Kevin N. Lawanto, David Clark. (2013). Pattern of Task Interpretation and Self-Regulated Learning Strategies of High School Students and College Freshmen during an Engineering Design Project.Journal of STEM Education Vol. 14 Issue 4 October-December 2013.

Livingston, J. A. (1997). Metacognition: An Overview. Retrieved from http:// www.gse.buffalo.edu/fas/shuell/cep564/Metacog.htm

Loch, Birgit & Catherine McLoughlin. (2011). An instructional design model for screencasting: Engaging students in self-regulated learning. Proceedings ascilite 2011 Hobart: Concise Paper.

Montgomery, Susan M & Linda N. Groat (1998).Students Learning Styles and Their Implications for Teaching.CRLT Occasional Paper No. 10.The University of Michigan. 1998.

Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching, and assessing.Theory into Practice, 41(4), 219-225.

Rowe, Frances A. & Jennifer A. Rafferty. (2013). Instructional Design Interventions for Supporting Self-Regulated Learning: Enhancing Academic Outcomes in Postsecondary E-Learning Environments. MERLOT Journal of Online Learning and Teaching. Vol. 9, No. 4, December 2013.

Santoso, Harry B, OenardiLawanto, Kurt Becker, Ning Fang, and Edward M. Reeve.(2014). High and Low Computer Self-Efficacy Groups and Their Learning Behavior from Self-Regulated Learning Perspective While Engaged in Interactive Learning Modules. Journal of Pre-College Engineering Education Research 4:2 (2014) 11–28.

Schraw G., Brooks D. W., &Crippen, K. J. (2005).Using an interactive, compensatory model of learning to improve chemistry teaching.Journal of Chemistry Education, 82, 637-640. Retrieved from http://www.immex. ucla.edu/docs/publications/pdf/multimethodCERP.pdf

Veenman, M. V. J., Elshout J. J., & Meijer, J. (1997).The generality vs. domainspecificity of metacognitive skills in novice learning across domains.Learning and Instruction, 7, 187-209.

Vos, Henk& E. De Graaff.Developing metacognition: a basis for active learning.European Journal of Engineering Education Vol. 29, No. 4, December 2004, 543-548.

Young, Dawn &Katrhryn Ley. (2005). Developmental College Student Self-Regulation: Results from Two Measures. Journal of College Reading and Learning, 36 (1), Fall 2005.

Zimmerman, B. J. (2000). Attaining self-regulation: a social cognitive perspective. In M. Boekaerts, P. R. Pintrich& M. Zeidner (Eds.), Handbook of Self-Regulation (pp. 13-39). New York: Academic Press.