Integrating Computational Thinking Concept Into Digital Storytelling to Improve Learners’ Motivation and Performance

Abstract

This paper examines a method which can be used by instructors pursuing innovative methods for language teaching, which expands learners’ motivation in second language learning. Computational thinking (CT) is a problem-solving skill which can motivate students’ English language learning. Designing a learning activity which integrates CT into English language learning has been considered in only a few academic studies. This study aimed to explore whether integrating CT into English language learning can be useful for improving learners’ motivation and performance. The method of “present, practice, and produce” was applied as a method of presenting computational thinking in the English language learning classroom. Fifty-two elementary school students (52) participated in the experimental study. Following an experimental design, data were collected and analyzed from a combination of knowledge test scores, storytelling, motivation, and anxiety surveys. The experimental results indicate that the CT strategy improves students’ language learning and raises their motivation in the two dimensions of extrinsic and intrinsic goal orientation. These results imply the positive effect of CT strategy on strengthening problem-solving skills of students participating in digital storytelling and increases their motivation and performance in English language learning.

Keywords

computational thinking digital storytelling scratch anxiety motivation

English has been recognized worldwide as the official language for international communities which do not share a common language. However, non-native speakers recognize language learning as an arduous task, and it is often difficult for learners to become competent in English (Cheng, 2013). Many educators persistently strive to motivate students who are learning English (Kondo et al., 2012), but it is a difficult task. In recent years, digital storytelling (DST) has been used as an effective approach to language learning. It is a powerful format because it provides opportunities for integrating technology with storytelling and enables students to represent their own understanding while combining narratives with images and verbal content. The findings of researchers indicate that DST, when conducted through a suitable pedagogical process, can increase student learning performance and motivation (Huang et al., 2017; Liu et al., 2018; Yang & Wu, 2012). Writing scripts identified by Yang and Wu (2012) as an important part of the DST process. However, studies also indicate that some students have difficulties while creating a digital story (Durak, 2018). Ohler (2006) claimed that many students have a problem with two of the key components of digital storytelling: story mapping and script writing. DST can be complicated, challenging, and may also cause anxiety for susceptible students (Liu et al., 2018).

Recently, computational thinking (CT) has been suggested as a learning strategy to improve English language learning. Using CT strategies to teach language skills can remove these drawbacks from DST (Vinayakumar et al., 2018). CT is a method of reasoning that teaches students how to solve complex problems with strategies that computers use (Wing, 2006). Integrating CT in DST helps students to improve the structure of their sentences in language writing through providing a designated purpose for grammar acquisition (Weng & Wong, 2017). The results of some recent studies which have incorporated computational thinking into English language learning indicate that using a CT strategy in the classroom can have a positive impact on students’ performance and motivation (Burke & Kafai, 2012; Vinayakumar et al., 2018). The insertion of CT into education gives individual responsibility to students, which enhances their motivation by encouraging student-student interaction (Kwon & Kim, 2018).

While recent researches have focused on the initial development of CT (Lye & Koh, 2018; Silva et al., 2018; Tran, 2019), relatively few studies have investigated the combination of digital storytelling and computational thinking strategies (Weng, 2017). To date, CT strategies have not been widely applied to assist English Language Learning (ELL), and few studies have discussed the application of CT in English teaching and learning (Jacob et al., 2018). Therefore, this study addresses this gap by integrating CT into DST to help determine whether there is a signiﬁcant association between CT-based DST and English language learning motivation. An experiment was conducted to investigate the effectiveness of such a learning approach on learners’ motivation and performance. This study's teaching strategy provides a scaffold for students by applying the computational thinking strategy to scriptwriting to facilitate the storytelling process.

Based on the literature and this study’s purpose, the following research questions are posed:

RQ1. What is the change in the level of students’ English language performance when integrating CT into the performance of DST tasks?

RQ2. What are the students’ motivation and anxiety in relation to English language learning when integrating CT and DST tasks?

Literature Review

Computational Thinking

Researchers and educators have identified computational thinking (CT) as an approach that uses computer science concepts to develops analytical and problem-solving skills in learners, which are crucial for students to study and live more efficiently in the 21st century (Tran, 2019; Voskoglou & Buckley, 2012; Zhong et al., 2016). According to Wing’s definition (2006), computational thinking involves four elements: “decomposition, pattern recognition, abstraction, and algorithm design”. Decomposition means breaking down a problem into smaller parts; pattern recognition means looking for patterns in those sub problems; abstraction means figuring out what information is needed to solve the problem; and algorithm design means developing a step-by-step means of achieving the solution (Wing, 2006). Educational researchers have been actively seeking innovative methods and approaches for integrating CT into the curriculum and engaging students in computational thinking (Bower et al., 2017).

The literature has confirmed that computational thinking can improve language skills, including sentence construction ability (Yadav et al., 2016), and writing (Fronza & Gallo, 2016). CT provides a systematic way for the sentence structure of the English language to create the stories in language writing (Vogel et al., 2019). CT could be integrated into language arts’ curricula by representing patterns and through the linguistic analyses of sentences to identify different sentences structure (Yadav et al., 2016). We use algorithms every day in our lives from following a cooking recipe to giving directions from point A to point B. For instance, students in second language classes could learn about algorithms by using cooking recipes or breaking down a simple daily task, such as brushing teeth, into steps (Burke & Kafai, 2012). Computational thinking can help achieve didactic objectives in language learning. CT lets learners become more conscious of language structure. Moreover, CT will allow them to identify the critical points in the English language writing (Fronza & Gallo, 2016).

Scratch: Promoting Computational Thinking

The MIT Media Lab firstly developed a graphical programming language named Scratch. Scratch is a programming tool that illustrates a promising approach to introducing students to aspects of computational thinking. Scratch provides learners a means of creating animations and interactive stories which can be shared online with other members of the Scratch community (Resnick et al., 2009). Scratch tool can be applied as a programming language to determine the impacts of computational thinking integration into English dialogue learning, bridging CT and Language Arts, which increase the English language learning motivation of elementary school students (Weng & Wong, 2017). Scratch is identified as the most used products to promote computational worldwide (Gretter & Yadav, 2016). The digital story includes more than one character (In Scratch called sprite) and scenes. Scratch allows the user to program these characters for dialog, triggering actions, and more easily drag and drop and snap it together in the script area separately (Vinayakumar et al., 2018).

Previous scholars such as Vinayakumar et al. (2018), Burke and Kafai (2012), and Weng and Wong (2017) have utilized Scratch as a computational thinking tool in developing their language activities. Vinayakumar et al. (2018) utilized Scratch software for engaging children in DST. Students used Scratch to develop some digital stories with computational practices, including creating, storyboard, locating supporting stuff, integrating sprites, sharing, and feedback. They found that DST and writing skills were more memorable and exciting for children engaged in Scratch learning activity. Another study by Burke and Kafai (2012) investigated the effect of Scratch programming on writing skills learned by middle school students to enhance their digital stories. Students used Scratch as an alternative environment to the learn language skill of composition to generate narratives based on individual expression and personal reflection. The results indicated that Scratch could provide an effective writing structure for middle school students to learn digital composition with drafting, revising, and publishing (Burke & Kafai, 2012). Weng and Wong (2017) conducted a qualitative study to explore the impact of incorporating CT into the learning of English dialogue through Scratch software. The findings of data collection via observation and interviews on nine students indicated that introducing computational thinking into English dialogues can increase students’ interests toward English learning (Weng & Wong, 2017). The experimental results of previous studies indicate that the remedial instruction of using Scratch can reduce student’s computer programming anxiety, and keep student's learning motivation of computer programming. The students’ main source of anxiety about learning English comes from tests (Liu et al., 2018). The students’ main source of anxiety about learning English comes from tests (Liu et al., 2018).

Moreover, it is crucial that CT be taught to students early in their education as it gives them an advantage in their future careers (Folk et al., 2015; Wing, 2008). While many recent studies have applied CT with high school students, only a few experiments have investigated the impact of CT on young children (Lye & Koh, 2018). Therefore, this study accomplished an experiment to examine the performance, motivation, and anxiety aspects of elementary school students towards DST and English language learning when using CT.

Digital Storytelling

In pedagogy, storytelling is identified as an operational instruction approach for young English language learners (Lotherington & Jenson, 2011). Combining digital literacy and multimodal design with storytelling is defined as Digital storytelling (DST) (Istenic Starčič et al., 2016). The storytelling process can help develop students language skills in reading, listening, writing, and speaking (Trawick-Smith & Smith, 2014). Including storytelling in learning activities fosters students’ motivation (Padilla-Zea et al., 2014). Integrating technology with storytelling provides in-depth learning for students through the synthesis of imagery and verbal representations with their own understanding (Kim, 2014; Lotherington & Jenson, 2011; Mayer, 2003).

DST has the potential beneﬁts of improving students’ language skills, including listening comprehension (Yoon, 2013), writing abilities (Sarıca & Usluel, 2016), oral abilities (Tahriri et al., 2015), and sentence creation skills (Kim, 2014; Tsou et al., 2006). The multimedia feature of DST has other advantages that can improve students’ learning motivation by capturing their attention, increasing their commitment towards learning activities, and fostering their learning interest (Di Blas et al., 2009; Hung et al., 2012; Tsiviltidou & Vavoula, 2017). Previous studies introduced four phases of storytelling. These are “1) pre-production; 2) production; 3) post-production; and 4) distribution” (Chung, 2006; Kearney, 2009; Ohler, 2006; Robin, 2006). The phase of pre-production in the model developed by Yang and Wu (2012) has ﬁve steps: ‘1) presenting the question, 2) discovering relevant information, 3) script writing, 4) telling the story orally, and 5) planning a story map and developing a storyboard.

Liu et al. (2019) performed a longitudinal investigation of the effects of digital collaborative storytelling on elementary learners' English learning performance who participated in a digital collaborative storytelling activity. They found that the proposed approach could improve students’ oral reading proficiency and engagement in social activities (Liu et al., 2019). A study conducted by Theune et al. (2013) examined the impact of mobile DST on collaborative learning. Their study results indicated that the approach facilitated creative problem-solving activities for the experimental group students and improved their motivation more than the control group, which used a traditional learning approach (Theune et al., 2013). Another prior study found that the incorporation of the DST approach in an English class significantly increased the creative thinking skill of the experimental group students when compared to the sixth-grade students who engaged in traditional lectures (Chiang et al., 2016). Despite the importance of storytelling in language learning, none of the previous studies could provide a comprehensive strategy for teaching English language learners how to find the necessary information for writing a sequence of events to produce a logical story.

Method

A one-month empirical study was administrated with 52 fifth-grade students in southern Taiwan to examine the impact of integrating CT into digital storytelling on the learning, motivation, and anxiety of elementary school students.

Participants

A total of 52 fifth-grade students, all of whom were taking an English class at a public elementary school in southern Taiwan, participated in this study. A quasi-experimental research design was applied, and one class, with twenty-seven students, was randomly designated to be the experimental group (N = 27). The other class, with twenty-five students, was assigned as the control group (N = 25).

Statistical power analysis was used to estimate the minimum numbers of participants for each group when group comparisons are made (Creswell, 2002). The Power analysis table (Lipsey & Aiken, 1990) was used in this study for assistance in determining the appropriate sample size for the experimental group (N = 27) and control group (N = 25).

The same examinations and course content were allocated to both classes, but different instructional strategies were used in control group and experimental group. The control group students were taught using the present, practice, and produce (PPP) method and teacher-led content-based lectures, whereas the experimental group students were taught using the PPP method for integrating CT in DST. Teaching English to the students starts from third grade, so it was assumed that all students had a similar level of basic English. In addition, teachers began to teach computer courses to the students from second grade, so all of the students had one year of Scratch programming. None of the participants had prior DST experience.

Experimental Research Procedure

The experimental research procedure conducted in this study is displayed in Figure 1. Two English teachers with more than ten years’ experience in language teaching conducted all these study activities, including tasks design, evaluation, and analysis.

Figure 1.

Experimental Procedure, Motivated Strategies for Learning Questionnaire (MSLQ), Foreign Language Classroom Anxiety Scale (FLCAS).

Figure 2.

Breaking Down Family Members.

Before the experiment started, the researcher organized meetings and consultations about CT-based instruction, DST, the teaching plan, and strategies for learning motivation in order to provide deep understanding of the research procedure for the English teachers.

In the first two weeks, the experimental and control group students completed the knowledge pre-test, storytelling, MSLQ, and FLCAS. In addition, the English teachers presented to the students the process of the learning activity. In the main learning activity, the teacher used the activity sequencing model of the present, practice, production (PPP) (Lewis, 1993) for teaching the students participating in the experimental and control group for storytelling in the course of English language learning. The teacher used the three phases of the PPP teaching method in the experimental group to explain the subject matter, which is identified in this study as integrating CT into digital storytelling about family members. Table 1 describes the activities, procedures, and materials required for the teaching plan.

Table 1.

Teaching Plan for the Experimental Group.

Activity	Procedure	Materials and equipment
Phase: Presentation Activity: Listen and identify Timing: 45 minutes Contents: Family members Aim: To identify how to introduce family in a story through CT strategy CT attributes: Decomposition, Pattern recognition, abstraction, and algorithm design	The teacher introduced to the students, the CT attributes based on the general principles of family introduction and instructed them on how to use CT in writing a story with start and end points with fixed story points of family members.	PowerPoint
Phase: Practice Activity: Do (Fill up worksheets) Timing: 45 minutes Contents: Family members Aim: To introduce family embers	• The teacher distributed worksheets to the students. • Students filled up the blank places to practice how to write chapters in a story with fixed story points; in this case, breaking down the family members into smaller and manageable parts to describe and introduce them (age, job, likes, and dislikes), then observing patterns in describing family members, identifying the general principles for introducing family members, and finally developing the step by step instructions for introducing their family and writing a story about them.	Markers/Whiteboard, Paper (worksheet), Pen/Pencil
Phase: Production Activity: Perform (writing) Timing: 90 minutes Contents: Family members Aim: To know and use CT to write a story about family	• The teacher distributed the family storytelling worksheets to the students. • Students used CT to write a full story introducing his/her family (including introduction, body, conclusion)(45 min). • Then, students used Scratch software to develop and distribute their digital story (45 min). • Students used Scratch to distribute and share their written stories with other students and receive feedback.	Paper (worksheets) Internet-connected computer (one computer per student), Scratch software

Presentation phase: The teacher used a PowerPoint lecture to present the story topic to the students and explained how to use the CT strategy for digital storytelling during 45 minutes (Figures 2 to 5).

Practice phase: The students practiced the storytelling using the CT strategy during 45 minutes. The practiced CT strategies was included decomposition to break down the family members into smaller parts; Pattern recognition to observe patterns in describing family members with age, job, what he/she likes, and what he/she does not like; Abstraction to identify the general principles for introducing family members. In this phase, students acted as storytellers by writing chapters of a story. Students wrote chapters provided by the teacher in their worksheets, and started and ended with fixed story points including introducing yourself, your mother, your father, etc. (refer to Figure 4).

Production phase: After completing family chapters, students made adjustments to each chapter (abstraction) so that the entire story becomes logical and cohesive. In this stage, students could express their creative ideas in creating the family story by adding extra sentences based on their own knowledge and experience, according to the story topic. In this part, students applied the CT strategy that was learned in the presentation phase by their teacher. The process of melding the chapters and producing the full story involves pattern recognition, which included observing trends in describing family members (Figure 3), abstraction which they used logic to put a jumbled story together into correct sequences (Figure 4), and generalization (Figure 4). Students analyzed the process to generalize about how the storytelling process could be more effective and describe how they would revise the plan for another time (algorithm design). The tasks of this phase required more concentration on the story content and were paper-based.

Figure 3.

Pattern of Introducing Family Members.

Figure 4.

Principle Points for Introducing Family.

The students finally used Scratch software to develop and distribute their own stories digitally. After writing the story, digital stories were created using Scratch by the students in experimental group. Scratch provided an opportunity for students to utilize their real family pictures during the digital story creation, which can increase students’ motivation for storytelling. Students synthesized their own concepts with the story points provided by the teacher according to the computational thinking dimensions to produce the digital story in Scratch. Students had the option to add text-to-speech recognition and user interactivity when they created their stories in Scratch.

A similar procedure, including pre-test, storytelling, and post-test, was conducted on the control group, which is did not use the CT strategy or Scratch for storytelling. The students in the control group learned storytelling through traditional simple PowerPoint lecture and teacher-centered learning. The students had to write a story introducing their family. After completing the stories, the students orally presented their written stories to their classmates in class. A knowledge post-test, MSLQ, and anxiety survey were given to both the experimental and control groups during the last week of the experiment. In order to increase internal validity, different questions were asked on the pre-test and post-test (Creswell, 2002).

Computational Thinking Techniques Integrated Into Storytelling

Decomposition is a CT dimension that when applies to this research, asked students to break down the family members into smaller and more manageable parts, which in this case are father, mother, brother, sister, grandfather, and etc. Figure 2 is a screenshot of the decomposition and breaking down of the family members.

Pattern recognition includes observing patterns, trends, and regularities in describing family members. Figure 3 is a screenshot of the pattern used for introducing family members. In this lesson, pattern recognition is a repeated process of looking at introducing family members and extracting relevant information to define main ideas while creating models of observed patterns to test predicted outcomes. In this case the student introduces his/her brother with his age, job, what he likes, and what he does not like.

Abstraction, or data representation, includes identifying the general principles for introducing family members. Figure 4 is a screenshot of the principles for introducing family members. This part provides start and end points with fixed story points of family members (introduce yourself, introduce your mother, introduce your father, etc.).

Algorithm design includes using computational thinking to develop an algorithm that allows students to follow step by step instructions for introducing their family and writing a story about them. Figure 5 is a flowchart of instructions for family storytelling. In this case, they chose family members to introduce and remove conflicting features such as iteration sentences to develop the instructions to solve similar problems and repeat the process. These steps are repeated until done, and finally writing a story conclusion.

Figure 5.

Flowchart of Family Storytelling Instructions.

Material and Tools for Learning Outcomes Measurement

Knowledge Test

The English teachers designed an English knowledge exam according to the content taught in the English class for storytelling. The created test aimed to assess students’ language skills and their applied CT abilities in reading comprehension, as well as evaluate their word choices in the context of storytelling using CT. The test consisted of a total of 35 questions, including ten close test items for testing grammar and reading comprehension, fifteen matching test items for vocabulary testing, and ten transformation test items for syntax and grammar testing. Each question was worth 1 point. The knowledge pre- and post-tests were graded by the English teachers. The validity of the tests was assessed by the English teachers. In addition, a reliability test was utilized to assess the Cronbach’s α, which was estimated to be 0.77 and 0.79 for the pre- and post-tests, respectively. These results indicate good reliability (Chin, 1998).

Story Evaluation Rubric

The effectiveness of the CT approach on students’ storytelling skills was assessed using an adopted scoring rubric to measure the pre- and post-test storytelling of students in the control and experimental groups. A Persuasive Essay Rubric (Andrade et al., 2008) was used to evaluate the stories created by the students in the control and experimental groups. In the storytelling pre-test, students of both groups were asked to write a story related to their daily life, and in the storytelling post-test, students wrote a story to introduce their family. The pre- and post-tests questions were created by the English teachers.

Scratch for CT

The CT process's final phase is writing a program to represent the algorithm in a language understandable for a computer. This study used the visual programming tool called “Scratch” for the CT. Scratch is a visual programming environment which offers a website where users can share their projects and exchange ideas or suggestions with other students (Resnick et al., 2009). Scratch allows users to build their stories and give their interactive presentations. A screenshot of a Scratch project (Scripts for introducing mother character) developed by a student is shown in Figure 6.

Figure 6.

Screenshot of a Scratch Project for Introducing Mother Character.

Dr. Scratch assessment tool (Moreno-León & Robles, 2015) was employed to analyze the applied CT strategy in digital stories created by students in Scratch software. Dr. Scratch is an open-source web application that analyzes Scratch projects to offer feedback to learners and educators and assign a CT score to the projects. Dr. Scratch identified as a CT assessment tool designed from a formative perspective (Román-González et al., 2017).

Motivation Survey

The Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich, 1991) was employed to recognize the effectiveness of integrating CT and DST in raising students’ learning motivation before and after the learning activity. The two motivational dimensions of the adopted questionnaire, including intrinsic and extrinsic motivation, were applied in this study's distributed questionnaire for gathering students’ perceptions regarding language learning motivation. These two dimensions were chosen because of the close relation of computational thinking and DST with intrinsic and extrinsic motivation (Jiang & Wong, 2018; Kotini & Tzelepi, 2015; Liu et al., 2018 ). As Kwon and Kim (2018) have stated, education based on Computational Thinking stimulates intrinsic motivation through learner-content interaction and active participation by giving individual responsibility to learners. While increased interest and curiosity as measured from students’ opinions regarding the English learning task indicate intrinsic motivation, being graded and evaluated by others identified as extrinsic motivation, which contributes to working harder at English learning tasks (Pintrich et al., 1993).

The motivation questionnaire contained four questionnaire items regarding intrinsic goal orientation and four questionnaire items about extrinsic goal orientation. The questionnaire used a five-point Likert scale. To confirm that all items included in the questionnaire would be understandable for students, the experienced English teachers used Inter-rater reliability to review the questionnaire. The teachers had a high degree of agreement (over 75%) on the questionnaire. The overall reliability of the questionnaire was found to be .86 and the Cronbach (alpha) of the intrinsic and extrinsic dimensions came out as .86 and .91, representing good reliability of the questionnaire (Nunnally, 1994). The motivation questionnaire was administrated to the participants in both the experimental and control groups before and after the experiment.

Foreign Language Classroom Anxiety Scale

The anxiety level of the participants was evaluated using the Foreign Language Classroom Anxiety Scale (FLCAS) (Horwitz et al., 1986), which was administered before and after the experiment. The FLCAS encompasses anxiety attributes related to the effects of the students' attitudes as well as his or her speaking and the exams given in language courses. This measurement tool included 33 questions each of which used a five-point Likert scale. The FLCAS included 33 question items with a 5-point Likert scale. After the results were collected, it was found that the lowest score was 33 and the highest score was 165, and students with a higher number indicating greater anxiety. The reliability of this scale indicates a high internal consistency, with a Cronbach’s alpha of .81.

Results

Learning Outcomes for Integrating CT to DST (RQ1)

Knowledge Test

A covariance analysis (ANCOVA) was utilized to assess whether the students in the experimental group (which used the CT strategy for DST) had higher test scores than students in the control group. Analysis of covariance (ANCOVA) was used because there were two-group pre-test/post-test with small sample sizes. The scores on the pre-test were treated as a covariate to control for pre-existing differences between the groups.

The results indicate a significant difference between the control and experimental group scores on the knowledge test, F (1, 52) = 14.187, p = .000. Table 2 represents the means and standard deviations for the control and experimental group results on the knowledge test, before and after controlling for storytelling. As is evident from this table, there is a significant difference between the control group scores and that of the experimental group that used the CT strategy for DST.

Table 2.

Knowledge Pre-Test and Post-Test Results for the Control and Experiment Groups.

		Pretest		Posttest		ANCOVA
Groups	N	Mean	SD	Mean	SD	Adj.M	F (1, 49)
Control	25	29.04	6.08	29.68	5.36	30.06	14.187***
Experiment	27	30.62	3.37	33.58	1.76	33.49

The maximum score for each test is 35, ***p <.001.

The experimental group students who used the CT strategy and Scratch for DST had higher scores than the control group students who utilized a traditional method of storytelling.

Storytelling

An adopted scoring rubric was used to evaluate the stories created by the students in the control and experimental groups. This rubric used a four-point rating scale from 1 to 4 for a total score of 26; it evaluated students’ stories using six criteria: organization, voice and tone, ideas and content, word choice, conventions, and sentence ﬂuency. To assess the impact of the CT strategy on digital storytelling, a covariance analysis (ANCOVA) was applied to the results of the storytelling pre- and post-test of students related to their daily life and introduce their family.

The analysis results indicate a significant difference between the control and experimental group in storytelling, F (1, 49) = 20.612, p = .000. The mean and standard deviations of students’ storytelling scores conducted before and after the experiment for the experimental and control group are presented in Table 3. As can be observed in the table, there is a significant difference between the control and experimental group depending on whether they used the CT strategy for DST. The outcomes reveal that students who used the CT strategy and Scratch for DST had higher storytelling scores than students utilized a traditional method of storytelling.

Table 3.

Storytelling Pre-Test and Post-Test Results for the Control and Experiment Groups.

		Pretest		Posttest		ANCOVA
Groups	N	Mean	SD	Mean	SD	Adj.M	F (1, 49)
Control	25	14.64	5.94	16.04	4.14	16.43	20.612***
Experiment	27	16.37	5.08	20.96	3.93	20.59

The maximum score for each test is 24.

***

p <.001.

Scratch Projects Analysis

This study utilized Scratch as a software system for digital storytelling which allowed students to express digitally their stories related to daily life and introduce their family by creating animations. In addition, Dr. Scratch adopted from Moreno-León and Robles (2015) was utilized as a formative assessment tool for analyzing students’ Scratch projects and graded their computational thinking skills in seven dimensions: Flow Control (FL), Data Representation (DR), Abstraction (AB), User Interactivity (UI), Synchronization (SN), Parallelism (PA), and Logical Thinking (LT). Dr. Scratch is a web application that allows teachers and students to automatically analyze projects coded in Scratch to check if they have been properly programmed, learn from their mistakes and get feedback to improve their code and develop their (CT) skills.

Table 4 represents ratings of competence level for each CT category in students’ stories. Each element in Dr. Scratch is deducted with 4 scores from 0 = null to 3 = proficiency. The overall computational thinking score achieved the median and mode of 9 and an average of 8.62. The overall CT score of this study is 7 (Basic level) which is calculated by adding up the partial scores of each CT criterion. According to Moreno-León and Robles’ (2015) projects with 7 points are classified as Basic user, projects between 8 and 14 points are Developing user, and more than 15 points can be considered Proficient user.

Table 4.

Rating of Competence Level for Each CT Concept in Students’ Stories (N = 27).

Statistical measure	Dimensions of computational thinking
Statistical measure	AB	PA	LT	SN	FC	UI	DR
Mean	1	1.14	0	2	1.92	1.55	1
Std.dev.	0	.53	0	0	.26	.50	0
Median	1	1	0	2	2	2	1
Mode	1	1	0	2	2	2	1
Min	1	1	0	2	1	1	1
Max	1	3	0	2	2	2	1

Figure 7 indicates the Dr. Scratch analysis results for a project with basic CT Score. The mean score for each of the computational thinking components is shown in Figure 8. As can been seen, the synchronization and flow control concepts obtained higher values, while logical thinking, abstraction, and data representation got lower values. Figure 8 indicates the Dr. Scratch analysis results for a project with basic CT Score.

Figure 7.

Dr. Scratch Analysis Results for a Project With Basic CT Score.

Figure 8.

CT Score Average of Students’ Scratch Projects.

Emotional Experiences of Students (RQ2)

Motivation

Table 5 indicates the results of the motivation questionnaire. An independent t-test was used to analyze students’ perceptions regarding motivation to language learning. The independent samples t-test procedure compared means for two groups of cases to calculate the similarity or differences between groups at the steps of the pre-test and the post-test.

Table 5.

Independent t-Test Results of Motivation Questionnaire.

Scales	Group	N	Mean	SD	t value	P value
Intrinsic goal	Experimental	27	4.38	.85	2.63*	.011
	Control	25	3.69	1.05
Extrinsic goal	Experimental		4.23	0.82	2.44*	.018
	Control		3.56	1.14

*p <.05.

The t values in Table 5 indicate significant differences in the mean scores for the Intrinsic goal (t = 2.63, p < .05) and extrinsic goal orientation (t = 2.44, p < .05) between two groups. The results reveal that the experimental group students that used the CT strategy for DST had a signiﬁcantly higher degree of motivation than the control group students in the two dimensions of intrinsic goal orientation and extrinsic goal orientation. This suggests that when the DST requires computational thinking strategies, this approach can reinforce the impact of intrinsic goals like interest and curiosity. This approach also motivated students towards extrinsic goal features like performance evaluation, rewards, and grades. The analysis results indicate that a majority of the students in experimental group found language learning using CT and Scratch attractive.

Foreign Language Classroom Anxiety

Table 6 shows the analysis results of the ANCOVA which was conducted on students’ pre-test and post-test FLCA scores to determine the impact of integrating CT and Scratch in DST on Foreign Language Classroom Anxiety (FLCA). The homogeneity assumption was supported by no interaction among the control and experiment group in the FLCA posttest (F = 2.838, p >.05). In the results there is a significant difference between the control and experiment group’s FLCA scores, F (1, 49) = 9.385, p = .004. In other words, students that used the CT strategy and Scratch for DST had less FLCA than those who utilized traditional methods of storytelling.

Table 6.

ANCOVA Results for the Anxiety Level of Pre- and Posttest of the Experimental and Control Groups.

		Pretest		Posttest		ANCOVA
Groups	N	Mean	SD	Mean	SD	Adj.M	F (1, 49)
Control	25	90.48	13.85	96.04	14.35	95.49	9.385**
Experiment	27	88.70	12.35	86.14	11.34	86.65

The maximum score for each test is 165; **p <.01.

The outcomes for the students of the experimental group are remarkably different than the students of the control group. It is because of that control group students that used the traditional method of storytelling felt more anxiety, whereas those use CT for DST felt less anxiety at the end of the storytelling activity. DST and Storytelling using Scratch encourage shy students to contribute in the learning activities while enhancing interaction between students.

The results of the FLCA scores of the students’ in-class presentations in the control group indicate that most students had problems with embarrassment and anxiety during the oral-presentation of their stories to their classmates. Moreover, the outcomes showed that the students who used CT and Scratch had less foreign language classroom anxiety than those used the traditional storytelling method. Anxiety about speaking a foreign language can have a debilitating effect on students’ learning performance because learners fear making mistakes in front of other people.

Discussion

In this study, the authors describe the integration of the computational thinking approach into digital storytelling as a technique for motivating students to learn English language. With the growth of integrating the CT approach into the curriculum, a lot of researches have been done on improving students’ problem solving skills, but none has focused on associating computational thinking and language learning (Aydeniz, 2018; Kong et al., 2018; Yadav et al., 2016).

The experimental results in this study indicate that integrating CT into DST provides an eﬀective learning mechanism for motivating students’ English language learning and enhancing their performance. Some prior research into digital storytelling are in line with the present study's results. For instance, a study conducted by Liu et al. (2018) studied the impact of the DST method on learning motivation and performance of 64 sixth grade students. The study observed the positive impact of the proposed DST on the language productivity of the experimental group. Another similar study tested the impact of individual work against group work on students’ emotions and self-sufficiency when engaged in digital storytelling. The findings of the study confirmed the important difference among students of individual and group work in their DST (Liu et al., 2018). Fronza and Gallo (2016) created an application for mobile devices that used CT in German language learning, but the tutorial did not consider the CT dimensions for problem solving in DST or analyze its beneﬁts for students.

In addition, this study’s results indicate that integrating CT strategy and using Scratch can enhance positive emotions and decrease anxiety regarding language learning. One reason for the lower anxiety of students that when CT and Scratch are used could be the logical thinking and problem solving support that CT provides in their DST tasks. As suggested by Wu et al. (2010), programming a course which uses Scratch as a game-based learning tool can overcome anxiety in learning (Wu et al., 2010). In particular, developing computational thinking with scratch in Mathematics curricula might have a constructive effect on mathematics performance of learners and reduce their anxiety levels (Calao et al., 2015). The findings of this study indicate that incorporating computational thinking reduces the anxiety of students by providing problem solving techniques they can use in their learning activities. These results are in line with the outcomes of prior studies (Israel et al., 2015; Lee et al., 2011).

Conclusion

This study designed a learning activity in which CT was integrated into English language learning. The experimental results indicate that CT-based English language learning is signiﬁcantly more eﬀective in enhancing students’ learning performance and motivation for language learning than traditional learning. Understanding computational thinking will give students a foundation for solving problems, and will be one of the fundamental core abilities in all steps of life in the 21st century. Motivating students to learn a foreign language is significant and challenging. Implementing CT-based DST activities bridges the gap between the classroom and real world, making English language learning more engaging, fun, and relaxing, as well as increasing motivation.

Two practical implications can be recognized from this study’s results. First, integrating CT into an English language learning course for digital storytelling is effective. Although storytelling as an efficient instruction method can develop the students’ English language abilities in writing and oral performance, it can be difficult for students, which may cause them anxiety. The outcomes of this study show that the CT strategy and Scratch software can provide an effective problem solving strategy during storytelling, which is able to reduce the negative emotions in students during digital storytelling. Additionally, CT and Scratch increase curiosity, interest, and interaction between students, which can lead to increases in their leaning motivation in regard to English language. Second, instead of using imagination in storytelling, CT can provide a logical scaffolding and structure for students when creating their stories in order to enhance their language learning performance. For instance, this study applied the four dimensions of CT strategy developed by Wing (2006) for developing digital storytelling in elementary school students. A CT-based learning task requires learners to decompose an issue into minor parts, look for patterns in those sub issues, figure out what information is needed, and develop the solution in a step-by-step process. A well-structured and supportive learning strategy can motivate students to solve problems and make decisions during the digital storytelling process.

Inspiration gained from this study can help academics and researchers to integrate CT strategies into their English language training to promote learning outcomes. Moreover, the findings may encourage instructors and scholars to implement CT curricula in elementary schools more than has been the case up until now. This study was conducted with a small sample size and all participants were fifth-grade students. This limits the outcomes’ generalization. A second shortcoming is the experiment’s duration. Further studies can be conducted to investigate the results for long-term retention tests. Finally, we make a suggestion that school administrators and teachers be required to understand what CT is and what it takes to bring it to the English learning classroom to ensure that students develop this ability more efficiently.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Ministry of Science and Technology, Taiwan, R.O.C. under Grant Number: MOST 107-2511-H-006-014-MY3 and 109-2511-H-006-011-MY3.

Author Biographies

Nadia Parsazadeh is currently a post-doctorate researcher at the Department of Electrical Engineering, National Dong Hwa University, Taiwan. Her research interests include computational thinking, online learning and its technology, usability testing, and instructional design.

Pei-Yu Cheng is currently a post-doctorate researcher at the Graduate School of Technological and Vocational Education, National Yunlin University of Science and Technology, Taiwan. His research interests include education technology, affective learning, sensing technologies in education, and learning analytics.

Ting-Ting Wu is currently an associate professor at the Graduate School of Technological and Vocational Education, National Yunlin University of Science and Technology, Taiwan. Her research interests include mobile and ubiquitous learning, information technology-applied instructions, and intelligent learning systems.

Yueh-Min Huang is currently a Chair professor at the Department of Engineering Science, National Cheng Kung University, Taiwan. His research interests focus on e-Learning, multimedia communications, artificial intelligence, and embedded systems.

ORCID iDs

Nadia Parsazadeh

Yueh-Min Huang

References

Andrade

H. L.

Wang

(2008). Putting rubrics to the test: The effect of a model, criteria generation, and rubric-referenced self-assessment on elementary school students’ writing. Educational Measurement: Issues and Practice, 27(2), 3–13.

Aydeniz

(2018). Integrating computational thinking in school curriculum. In Khine

M. S.

(Ed.), Computational thinking in the STEM disciplines (pp. 253–277). Springer.

Bower

Wood

L. N.

Lai

J. W.

Howe

Lister

Mason

Highfield

Veal

J. J. A. J.

, & O, T. E. (2017). Improving the computational thinking pedagogical capabilities of school teachers. Australian Journal of Teacher Education, 42(3), 4.

Burke

Kafai

Y. B.

(2012). The writers’ workshop for youth programmers: Digital storytelling with scratch in middle school classrooms [Symposium]. 43rd ACM Technical Symposium on Computer Science Education (pp. 433–438). SIGCSE.

Calao

L. A.

Moreno-León

Correa

H. E.

Robles

(2015). Developing mathematical thinking with scratch. In Gráinne

Tomaz

Christoph

Johannes

Lavoué

(Eds.), Design for teaching and learning in a networked world (pp. 17–27). Springer.

Cheng

C.-M.

(2013). Reflections of college English majors’ cultural perceptions on learning English in Taiwan. English Language Teaching, 6(1), 79–91.

Chiang

F-c.

Chiu

C-y.

Z-h.

(2016). Using digital storytelling to enhance elementary school students' creative thinking [Paper presentation]. 2016 International Conference on Advanced Materials for Science and Engineering (pp. 505–508). IEEE.

Chin

W. W.

(1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.

Chung

S.-K.

(2006). Digital storytelling in integrated arts education. The International Journal of Arts Education, 4(1), 33–50.

10.

Creswell

J. W.

(2002). Educational research: Planning, conducting, and evaluating quantitative. Prentice Hall.

11.

Di Blas

Garzotto

Paolini

Sabiescu

(2009). Digital storytelling as a whole-class learning activity: Lessons from a three-years project [Paper presentation]. Joint International Conference on Interactive Digital Storytelling (pp. 14–25). Springer.

12.

Folk

Lee

Michalenko

Peel

Pontelli

(2015). GK-12 DISSECT: Incorporating computational thinking with K-12 science without computer access [Paper presentation]. 2015 IEEE Frontiers in Education Conference (pp. 1–8). IEEE.

13.

Fronza

Gallo

(2016). Towards mobile assisted language learning based on computational thinking [Paper presentation]. International Conference on Web-Based Learning (pp. 141–150). Springer.

14.

Gretter

Yadav

(2016). Computational thinking and media & information literacy: An integrated approach to teaching twenty-first century skills. TechTrends, 60(5), 510–516.

15.

Horwitz

E. K.

Horwitz

M. B.

Cope

(1986). Foreign language classroom anxiety. The Modern Language Journal, 70(2), 125–132.

16.

Huang

Y.-Y.

Liu

C.-C.

Wang

Tsai

C.-C.

Lin

H.-M.

(2017). Student engagement in long-term collaborative EFL storytelling activities: An analysis of learners with English proficiency differences. Journal of Educational Technology & Society, 20(3), 95–109.

17.

Hung

C.-M.

Hwang

G.-J.

Huang

(2012). A project-based digital storytelling approach for improving students’ learning motivation, problem-solving competence and learning achievement. Journal of Educational Technology & Society, 15(4), 368–379.

18.

Israel

Pearson

J. N.

Tapia

Wherfel

Q. M.

Reese

(2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education, 82, 263–279.

19.

Istenic Starčič

Cotic

Solomonides

Volk

(2016). Engaging preservice primary and preprimary school teachers in digital storytelling for the teaching and learning of mathematics. British Journal of Educational Technology, 47(1), 29–50.

20.

Jacob

Nguyen

Tofel-Grehl

Richardson

Warschauer

(2018). Teaching computational thinking to English learners. NYS TESOL Journal, 5(2), 12–24.

21.

Jiang

Wong

G. K.

(2018). Are children more motivated with plugged or unplugged approach to computational thinking? [Paper presentation]. 49th ACM Technical Symposium on Computer Science Education (p. 1094). ACM.

22.

Kearney

(2009). Towards a learning design for student-generated digital storytelling. University of Wollongong.

23.

Kim

(2014). Developing autonomous learning for oral proficiency using digital storytelling. Language Learning & Technology, 18(2), 20–35.

24.

Kondo

Ishikawa

Smith

Sakamoto

Shimomura

Wada

(2012). Mobile assisted language learning in university EFL courses in Japan: Developing attitudes and skills for self-regulated learning. ReCALL, 24(2), 169–187.

25.

Kong

S.-C.

Chiu

M. M.

Lai

(2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178–189.

26.

Kotini

Tzelepi

(2015). A gamification-based framework for developing learning activities of computational thinking. In Kotini

Tzelepi

(Eds.), Gamification in education and business (pp. 219–252). Springer.

27.

Kwon

Kim

(2018). A study on the design and effect of computational thinking and software education. Thinking, 2(3), 4.

28.

Lee

Martin

Denner

Coulter

Allan

Erickson

Malyn-Smith

Werner

(2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32–37.

29.

Lewis

(1993). The lexical approach (Vol. 1). Language Teaching Publications.

30.

Lipsey

M. W.

Aiken

L. S.

(1990). Design sensitivity: Statistical power for experimental research (Vol. 19). SAGE.

31.

Liu

C.-C.

Yang

C.-Y.

Chao

P.-Y.

(2019). A longitudinal analysis of student participation in a digital collaborative storytelling activity. Educational Technology Research and Development, 67(4), 907–929.

32.

Liu

K.-P.

Tai

S.-J D.

Liu

C.-C.

(2018). Enhancing language learning through creation: The effect of digital storytelling on student learning motivation and performance in a school English course. Educational Technology Research and Development, 66(4), 913–935.

33.

Liu

M.-C.

Huang

Y.-M.

Y.-H.

(2018). Effects of individual versus group work on learner autonomy and emotion in digital storytelling. Educational Technology Research and Development, 66(4), 1009–1028.

34.

Lotherington

Jenson

(2011). Teaching multimodal and digital literacy in L2 settings: New literacies, new basics, new pedagogies. Annual Review of Applied Linguistics, 31, 226–246.

35.

Lye

S. Y.

Koh

J. H. L.

(2018). Case studies of elementary children’s engagement in computational thinking through scratch programming. In Khine

M. S.

(Ed.), Computational thinking in the STEM disciplines (pp. 227–251). Springer.

36.

Mayer

R. E.

(2003). The promise of multimedia learning: Using the same instructional design methods across different media. Learning and Instruction, 13(2), 125–139.

37.

Moreno-León

Robles

(2015). Computer programming as an educational tool in the English classroom a preliminary study [Paper presentation]. 2015 IEEE Global Engineering Education Conference (EDUCON) (pp. 961–966). IEEE.

38.

Nunnally

J. C.

(1994). Psychometric theory 3E. Tata McGraw-Hill Education.

39.

Ohler

(2006). The world of digital storytelling. Educational Leadership, 63(4), 44–47.

40.

Padilla-Zea

Gutiérrez

F. L.

López-Arcos

J. R.

Abad-Arranz

Paderewski

(2014). Modeling storytelling to be used in educational video games. Computers in Human Behavior, 31, 461–474.

41.

Pintrich

P. R.

(1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). National Center for Research to Improve Postsecondary Teaching and Learning.

42.

Pintrich

P. R.

Smith

D. A.

Garcia

McKeachie

W. J.

(1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813.

43.

Resnick

Maloney

Monroy-Hernández

Rusk

Eastmond

Brennan

Millner

Rosenbaum

Silver

Silverman

Kafai

(2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60–67.

44.

Robin

(2006). The educational uses of digital storytelling [Paper presentation]. Society for Information Technology & Teacher Education International Conference (pp. 709–716). Association for the Advancement of Computing in Education.

45.

Román-González

Pérez-González

J.-C.

Jiménez-Fernández

(2017). Which cognitive abilities underlie computational thinking? Criterion validity of the computational thinking test. Computers in Human Behavior, 72, 678–691.

46.

Sarıca

H. Ç.

Usluel

Y. K.

(2016). The effect of digital storytelling on visual memory and writing skills. Computers & Education, 94, 298–309.

47.

Silva

L. R.

da Silva

A. P.

Toda

Isotani

(2018). Impact of teaching approaches to computational thinking on high school students: A systematic mapping [Paper presentation]. 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT) (pp. 285–289). IEEE.

48.

Tahriri

Tous

M. D.

MovahedFar

(2015). The impact of digital storytelling on EFL learners’ oracy skills and motivation. International Journal of Applied Linguistics and English Literature, 4(3), 144–153.

49.

Theune

Linssen

Alofs

(2013). Acting, playing, or talking about the story: An annotation scheme for communication during interactive digital storytelling [Paper presentation]. International Conference on Interactive Digital Storytelling (pp. 132–143). Springer.

50.

Tran

(2019). Computational thinking equity in elementary classrooms: What third-grade students know and can do. Journal of Educational Computing Research, 57(1), 3–31.

51.

Trawick-Smith

J. W.

Smith

(2014). Early childhood development: A multicultural perspective. Pearson.

52.

Tsiviltidou

Vavoula

(2017). Digital storytelling as a framework for inquiry-based museum learning [Paper presentation]. 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT) (pp. 403–405). IEEE.

53.

Tsou

Wang

Tzeng

(2006). Applying a multimedia storytelling website in foreign language learning. Computers & Education, 47(1), 17–28.

54.

Vinayakumar

Soman

Menon

(2018). Digital storytelling using scratch: Engaging children towards digital storytelling [Paper presentation]. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1–6). IEEE.

55.

Vogel

Hoadley

Ascenzi-Moreno

Menken

(2019). The role of translanguaging in computational literacies: Documenting middle school bilinguals’ practices in computer science integrated units [Paper presentation]. 50th ACM Technical Symposium on Computer Science Education (pp. 1164–1170). ACM.

56.

Voskoglou

M. G.

Buckley

(2012). Problem solving and computational thinking in a learning environment. arXiv preprint arXiv:1212.0750.

57.

Weng

(2017). Impact of infusing computational thinking into English dialogue learning. HKU Theses Online (HKUTO).

58.

Weng

Wong

G. K.

(2017). Integrating computational thinking into English dialogue learning through graphical programming tool [Paper presentation]. 2017 IEEE 6th International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (pp. 320–325). IEEE.

59.

Wing

J. M.

(2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

60.

Wing

J. M.

(2008). Computational thinking and thinking about computing. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 366(1881), 3717–3725.

61.

W.-Y.

Chang

C.-K.

Y.-Y.

(2010). Using Scratch as game-based learning tool to reduce learning anxiety in programming course. In Global learn (pp. 1845–1852). Association for the Advancement of Computing in Education.

62.

Yadav

Hong

Stephenson

(2016). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60(6), 565–568.

63.

Yang

Y.-T C.

W.-C I.

(2012). Digital storytelling for enhancing student academic achievement, critical thinking, and learning motivation: A year-long experimental study. Computers & Education, 59(2), 339–352.

64.

Yildiz Durak

(2018). Digital story design activities used for teaching programming effect on learning of programming concepts, programming self-efficacy, and participation and analysis of student experiences. Journal of Computer Assisted Learning, 34(6), 740–752.

65.

Yoon

(2013). Are you digitized? Ways to provide motivation for ELLs using digital storytelling. International Journal of Research Studies in Educational Technology, 2(1), 1–10.

66.

Zhong

Wang

Chen

(2016). An exploration of three-dimensional integrated assessment for computational thinking. Journal of Educational Computing Research, 53(4), 562–590.