A Technology-Enhanced Intervention for Self-Regulated Learning in Science
by Tali Berglas-Shapiro, Bat-Sheva Eylon & Zahava Scherz - 2017
This article describes the development of a technology-enhanced self-regulated learning (Te- SRL) environment designed to foster studentsí SRL of complex science topics. The environment consists of three components, one of which is a specially designed computerized system that offers students a choice between different types of scaffolding and encourages them to make multiple choices regarding the paths that they take when planning their learning. We describe a three-year mixed-methods study aimed at observing learnersí use of SRL processes when learning in the Te-SRL environment. This article focuses on nine case studies that were selected from 630 seventh-grade students who participated in the study. Data were obtained from assessment tasks, self-evaluation forms, think-aloud protocols, and interviews; data were traced from the computerized system. The findings suggest that students can improve over time in regulating their learning and in utilizing learning skills in a computerized system when provided with opportunities to practice, along with scaffolding. Many students, however, did not take full advantage of these scaffolding opportunities and seemed to lack the high-level skills needed for seeking information on the Internet. These findings underscore the importance of developing a culture of learning with technology-enhanced learning environments and scaffolding SRL in such environments.
During a study that required students to solve complex scientific problems presented to them in a computerized environment, Tom, a seventh-grade student, sat staring at the computer screen trying to answer the following question: Two students are preparing to go on a trip to Lapland. In the following section, you will be required to help them prepare for this trip. Before embarking on this journey, answer the following question:
Where is the region of Lapland located?
The northern part of North America
The northern part of Europe
The southern part of South America
The northern part of Asia
The problems instructions specifically indicated that the students could use the web as a resource. After typing the question into the Google search engine, Tom (T) clicked on the first resource presented in the index, which led him to the Wikipedia page on Lapland. He stared at the screen for a few moments and closed the page. The observing researcher (R) asked Tom to explain his actions, to which he replied, I did not find what I wanted.
R: What do you need to find?
T: I do not remember.
R: What should you do now?
T: [Thinks for a few moments]. I will read the question again.
[Tom rereads the question and goes back to the Wikipedia page. He once again closes the page.]
T: I did not find where Lapland is located.
R: Did you search the page for information?
T: There is too much information. How can I find what I need among so many words?
R: Did you notice the map?
T: [opens the page again and stares at the map] I do not know what this map means.
After a few minutes, Tom goes back to the question. He reads the distractors and marks distractor (a).
This scenario is one of many of our observations in depicting students difficulties while learning and solving problems with a computerized environment, which can be attributed to one or more of the following:
Lack of content knowledge
Inadequate learning skills and/or difficulty in implementing skills such as reading comprehension and map reading
Inefficient self-regulation of learning, such as setting clear targets, implementing strategies needed to achieve the target, and monitoring learning
Insufficient information and communication technology (ICT) skills, such as effective methods of information seeking on the web
Such observations of students activities while learning with computerized environments shed light on currently needed educational objectives. Over the last few decades, educational thinkers from all over the world have contended that in the past, the undue emphasis on the content taught led to rigid instruction (Barrie 2007; Wandera, 2014) and that the central focus of pedagogy should be on developing learning skills. An additional driving force for focusing on attaining adequate learning skills in the 21st century is due to the nature of work in modern Western societies, which has altered beyond recognition. Knowledge and skills that were once valued are changing (e.g., Alexander, 2002; Beetham & Sharpe, 2013; Facer, 2011; Silva, 2009), and contemporary jobs call for mastery of literacy and numeracy, adaptability, problem solving, communication, advanced levels of ICT use and other learning skills, rather than the mastery of knowledge per se (Barrie, 2007; Beetham & Sharpe, 2013; Wandera, 2014). Accordingly, todays learners are encouraged to be active and self-directed participants in the learning process of knowledge and skills, taught in an integrated manner (Berglas-Shapiro, Eylon, & Scherz, 2013; Waring & Evans, 2015). Despite the ongoing debate regarding the balance between knowledge and skills there is a general agreement that students need a broad content base as well as skill-related instruction - learning how to learn - and that the two aspects are intertwined (Greiff et al., 2015). This approach is reflected in several reports and curricula worldwidefor example, the Final Report of the National Mathematics Advisory Panel (National Mathematics Advisory Panel, 2008) and the National Research Councils (NRC) Framework (NRC, 2012).
Another current educational objective focuses on integration of technologies into contemporary learning environments. The multiplicity of learning technologies beyond the classroom and away from the teacher may open up new opportunities for education. Digital technologies can alter the relationship among the teacher, the learners, and the curriculum; in an age with burgeoning resources on the web and students increasing digital skills, education should prepare learners to take control of their own learning and to actively participate in the education process. In other words, todays education should emphasize and advance students self-regulation of learning.
Over the past years, the development of technology-enhanced learning environments (TELEs) has begun to enable active forms of learning rather than more receptive learning based on books and lectures. These developments include social media, modeling, tracking and fostering of learners metacognitive and self-regulatory behaviors, among others (e.g., Azevedo & Aleven, 2013). However, as shown in Toms case, digital native students may be able to use technologies, but that does not mean they can effectively use them for learning, just as knowing how to read and write does not promise successful and effective learning from books.
There is a growing need to identify the different self-regulatory behaviors of different types of students in TELEs and to observe their interactions with such environments in order to advance the design of supportive environments that will enable and foster self-directed and meaningful learning. This article describes the theoretical, empirical, and practical considerations for designing a technology-enhanced environment that supports self-regulated learning. Here we report on a qualitative research study that examined the self-regulatory actions of students who used technology-enhanced self-regulated learning (Te-SRL) for solving problems related to the seventh-grade science curriculum. Our findings shed light on essential high-order learning skills and SRL strategies that support meaningful problem solving in a technology-enhanced environment and indicate that working with the Te-SRL environment has an impact on the development of students metacognitive, motivational, and behavioral self-regulatory abilities.
In the following paragraphs, we will outline the theories and prior research, which formed the basis for designing our study.
INSTRUCTION OF SKILLS IN SCIENCE
A widely recognized current view is that one of the most important tasks of teachers today is to equip students with high-order skills that will support independent learning: for example, inquiry and problem-solving skills, thinking skills, and learning skills (Chipman & Segal, 2014; Spektor-Levy, Eylon, & Scherz, 2009; Stone, 2014). Among others, this view is mirrored in the P21s framework for 21st-century learning (e.g., Trilling & Fadel, 2009) as well as in the Next Generation Science Standards (NGSS), which are the K12 science content standards in many states of the United States (e.g., Bybee, 2014).
Approaches toward skills instruction in the classroom are varied and have changed dramatically over the years. Early approaches claimed that skills and capabilities develop by self-directed learning and by completing learning tasks throughout students studies (e.g., Bennett, Dunne, & Carre, 1999; Straka, Nenniger, Spevacek, & Wosnitza, 1996). In contrast is the explicit approach that assigns an essential role of skills development to direct, explicit, guided, and well-planned learning opportunities (Bailey & Heritage, 2014; Campbell, Kaunda, Allie, Buffler, & Lubben, 2000; Scherz, Michman, & Tamir, 1986). In addition, there is some controversy about the earlier detached skills teaching approach. Generally, there seems to be a current consensus in support of the contextual approach that advocates the instruction of skills in the classroom in the context of topics learned as an integral part of the learning activities.
The Learning Skills for Science (LSS) model developed by Scherz, Spektor-Levy, and Eylon (2005) is based on the explicit as well as the contextual approach to skills instruction. This LSS model involves structured, explicit, and spiral instruction of skills, that is, students should be aware of the process of skills acquisition and should reflect on it. The spiral instruction entails initially introducing students to different skills and subskills in depth, followed by practice in the course of their science studies.
SELF-REGULATION OF LEARNING
A general working definition of self-regulated learning (SRL) according to Pintrich (2003) is that SRL is an active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate, and control their cognition, motivation, and behavior, guided and constrained by their goals and the contextual features in the environment. Pintrichs model, which is the basis for the design of the TELE described in this article, characterizes SRL as involving at least four interdependent phases (forethought, monitoring, control/ management/ regulation, and reaction/reflection) used by students to manage and regulate their own academic functioning with regard to four areas (cognition, motivation and affect, behavior, and context /environment).
SELF-MONITORING AND SELF-EVALUATION
Flavell (1979) introduced and defined the construct of metacognition as knowledge of ones own cognitive processes and products. This concept is the basis for terms such as self-monitoring and self-evaluation. There are different approaches to the notion of monitoring in the educational literature. According to Hacker (1998), the term refers to making overt judgments or self-evaluations regarding the current and ongoing level of understanding and comparing them with objective measures. The ability to monitor one’s cognition and behavior is an important aspect of self-regulated learning. Only with accurate monitoring will students then be able to successfully and effectively regulate their learning (Greene, Dellinger, Tüysüzoğlu, & Costa, 2013).
Self-evaluations of progress help students focus on self-regulation processes and can raise and reinforce their motivation and self-efficacy (SE) in order to improve and develop. Research shows that allowing students to periodically evaluate their own learning capabilities increases their SE, motivation to self-regulate, and use of self-regulated learning strategies (e.g., Greene et al., 2013; Schunk & Swartz, 1993). Students should be taught how to evaluate their learning progress and be given opportunities to do so. Typically, in school, students have their learning evaluated for them by their teachers. Students need opportunities for self-evaluation because they may not do it automatically, and it affects their motivation and self-regulated learning (Schunk, 2001).
LEARNING WITH TELES AND IMPLICATIONS FOR SRL
In this article, we use the term technology-enhanced learning environments (TELE) to denote any form of learning environment in which ICT tools support and facilitate learning. In these environments, students acquire skills or knowledge, usually with the help of facilitators. More specifically, they learn about support tools and technological resources and can play an active role in their own learning process (Aleven, Stahl, Schworm, Fischer, & Wallace, 2003; Dettori, 2008; Land, 2000).
TELEs offer several advantages for promoting meaningful learning and are becoming increasingly common as learning environments used in the science classroom. Many such environments are learner centered: The users are responsible for controlling many aspects of their learning, the computer environment, and other aspects of the learning context. Such environments have the potential to be ideal learning environments (Jonassen & Reeves, 1996; Lajoie, 1993). Many TELEs also have embedded features designed to cognitively and meta-cognitively aid students learning (Bernacki, Aguilar, & Byrnes, 2011). However, these and other advantages are only beneficial for learners who have the requisite skills to self-regulate their learning when using TELEs in a classroom setting.
Recent technological developments have made it possible to design TELEs that have much potential for fostering SRL, and there is some empirical evidence that they actually do so (Beishuizen, 2008; Carneiro, Steffens, & Underwood, 2005; Steffens, 2006). Other research indicates that students often experience certain difficulties in regulating their learning in TELEs and therefore learn little (Azevedo & Cromley, 2004; Brush & Saye, 2001).
Another point of contention regarding the effectiveness of TELEs refers to the type of scaffolding or prompts offered in these environments. Because effective SRL involves making decisions (e.g., allocation of time, enactment of strategies ), removing the possibility of making decisions may obviate the need for the student to contend with difficulties and make important decisions regarding his or her learning, thus hindering his or her development of SRL skills. On the one hand, several studies have provided evidence that learning with TELEs in the absence of scaffolding hinders students ability to regulate their learning and thus often leads to a failure to gain a conceptual understanding of the topics (Hill & Hannafin, 1997; Land & Greene, 2000). On the other hand, there is still disagreement about the type and the nature of the scaffolds needed. According to Way and Rowe (2008), the solution to better matching learning objects to diverse learner needs may lie in creating more complex, dynamic, and challenging learning objects and by incorporating appropriate flexible scaffolding rather than producing a series of learning objects that target specific learning outcomes.
The view that all learners will need the same scaffolding for learning tends to produce linear designs, and in some respects, this is incompatible with the constructivist paradigm. The presence of meta-cognitive scaffolding and optional conceptual and strategic scaffolding, available on demand, reflects the expectation of variation in learning needs. This view of learning is more likely to lead to learning tasks having greater complexity and to greater learner autonomy in terms of learning pathways.
DESCRIPTION OF THE TE-SRL LEARNING ENVIRONMENT
The Te-SRL environment adopts the concept of self-regulated learning as well as the need for scaffolding in TELEs (Azevedo, Verona, & Cromley, 2001; White, Shimoda, & Frederiksen, 2000). This environment includes three components (Figure 1): (a) assessment tasks for evaluating students achievements regarding the content and skills taught, (b) evaluation forms for self-reflection and objective reflection of the students success on tasks, and (c) a Te-SRL system.
Figure 1. The components of the Te-SRL environment
The Te-SRL system is generic and provides support to students in their assessment of learning tasks from various scientific areas. For the purpose of this study, we developed three units in the Te-SRL system, aimed at three different seventh-grade science topics (content and skills):
Unit 1 relates to macroscopic properties of matter + table skills
Unit 2 relates to microscopic properties of matter + graph skills
Unit 3 relates to characteristics of life + charts and illustration skills
Each unit in the Te-SRL system starts with an introduction that describes the required learning skills and contents in the unit as well as the alternative scaffolds that are available to students (Figures 2 and 3).
Each unit includes four problems embedded in a background story. The system offers the students three different paths for solving each problem: (a) solving the problems without any scaffolds (Direct path), (b) solving the problems with a hint (Hints path) accessible to the student by clicking an icon, and (c) solving the problems with help and guidance (Hints & Guidance path). Each path rewards students with a different number of points in order to simulate computer and television games familiar to the students, where one can access help in return for points. Students start by selecting one of the scaffold options for solving a given problem. They are allowed to change their choice of scaffolding before each of the following problems. The problems in all three paths are identical, as are the solutions.
Figure 2. Introduction page of a Te-SRL system unit presenting the alternative scaffolds available and requisite skills and content necessary (when clicking the For more information icon)
Figure 3. Example of nonembedded hints and embedded guidance in a Te-SRL system
Over the past three years, we have conducted a study to investigate the self-regulating actions and considerations of seventh-grade students, which were applied when using the Te-SRL environment. This article elaborates on the qualitative research that followed the development process of students self-regulated learning strategies while solving problems in the Te-SRL system. Quantitative aspects of the study are reported elsewhere (Berglas-Shapiro, 2015).
Of the 630 seventh-grade students from 6 schools and 13 classes who participated in our study (see Berglas-Shapiro, 2015), we randomly chose 9 students from different classes for the qualitative study. Here we will show the main trends found among these 9 students and will describe in depth 3 of them.
Procedures and Tools
The students who participated in this research learned science according to a new curriculum, which incorporates skills learning into the science content in an explicit and spiral manner. Our study consisted of three topics studied throughout the school year and included two assessment tasks per topic (Figure 1). Upon receiving the graded assessment tasks, the students were asked to fill out a self-evaluation form about their performance in the task and received an objective evaluation of their performance from the teacher. After learning each of the three topics, the students were asked to do a related Te-SRL system activity (Units 1, 2, and 3, respectively). The system traces student actions and provides data about students responses, the duration of execution of each step in the project, the use of different scaffolds, and more.
While working with each Te-SRL system unit, we asked 9 students to think aloud, namely, to verbalize their thinking processes and actions. Because most students had a hard time verbalizing their activities, we used the questions and prompting method (Someren, Barnard, & Sandberg, 1994). This method allows the experimenter to ask questions and to prompt the student during the problem-solving process. Data were also collected from students assessment tasks, from their self-evaluation forms, and from reflective questions incorporated into the Te-SRL system units.
The data from the think-aloud protocol were analyzed according to a framework we developed for this purpose and were validated by four colleagues. The initial processes and features for the framework were based on the Motivated Strategies for Learning Questionnaire (Pintrich, Smith, Garcia, & McKeachie, 1993), the Learning and Study Strategies Inventory (LASSI; Weinstein, Schulte, & Palmer, 1987), and Zimmerman (2000). The framework was modified a posteriori according to students actual statements. Students performance on all three Te-SRL units was evaluated and graded using the Te-SRL systems scoring and tracing ability.
The aim of this research is to follow the development process of students self-regulated learning while they solve problems using the Te-SRL system. We will refer to three categories of SRL features: metacognitive, motivational, and behavioral (see Zimmerman, 1990). We will report on seven SRL strategies and features that emerged in the full study as statistically significant concerning the choices that all of the 670 students made in the system and students performance using the Te-SRL system units (Berglas-Shapiro, 2015). Measures for the self-evaluation skills feature were based on actual students performance, whereas the other six SRL strategies and features were assessed by analyzing reflective questions that were incorporated into the Te-SRL system units and the think-aloud protocols of 9 students.
1. Self-evaluation skills: Self-evaluation skills denote students awareness of their cognition and their mastery of skills. Students who lack this ability may have difficulty making good learning choices. Students self-evaluation skills were measured by comparing their self-evaluation reports regarding their results in six assessment tasks throughout the research period, with objective evaluation reports prepared by the researchers.
2. Knowledge of cognition: This feature refers to self-awareness of what one knows and/or does not know, such as the cognitive reasons for choosing a learning path in the Te-SRL system.
3. Self-efficacy: In the context of solving problems in the Te-SRL system, this feature refers to ones belief in the ability to successfully carry out the actions needed to solve a problem in the system.
4. Learning from experience: This feature relates to students ability to take into account past experiences when making choices in learning situations.
5. Use of help (scaffolding): The Te-SRL system has a tracing system. It traced students use of the hints (whether or not the student clicked on the icon). In addition, we collected data from the reflective questions (we asked students whether they read the hints and to what extent the hints helped them) and from observations of the 9 students.
6. Use of the web: At the beginning of each unit, we informed students that they could always use the web as a resource for information during the activity.
7. Strategic planning and use of learning strategies: This feature refers to planning ones steps and actions in a learning situation and implementing strategies to achieve ones goals.
Observations and transcripts of students actions and think-aloud protocols during Te-SRL system Units 1, 2, and 3 enable an in-depth understanding of students behaviors. The following descriptions and excerpts of 3 students protocols (Oren, Beth, and Adam) exemplify the development process of their SRL strategies while using the Te-SRL system over time.
Oren is a high-achieving student. Data from his self-evaluation forms show a vast improvement of his skills over time. In Unit 1, Oren had a hard time verbalizing his thoughts and actions and needed a lot of prompting. There are indications that his SE varies. On the one hand, there are statements that suggest high SE: I am very good at using computers. I am a wiz on the web and can find anything; however, on the other hand, there are statements suggesting his low SE: I cant solve problems without the teachers help. . . . how can I do it on my own? He continuously made judgments of perceived capabilities, suggesting that his SE improves over time: I know I can do it. Last time I tried to solve the problem this way and it went well. . . (Unit 2), I dont need the teacher. We learned this strategy in class so I can do it. . . (Unit 3). These statements refer explicitly to his ability to learn from experience and to implement learning strategies taught in class. Following his success in solving problems, he reported, I did well last time, so now I know I can do it, indicating that his SE strengthened.
When facing obstacles, Orens reaction was to try harder, a trend that increased from unit to unit. Oren had a high awareness of knowledge. He read the prerequisites for each problem and decided whether he had the ability to solve the problem. However, upon deciding, he chose to answer without help and did not implement the strategies needed. For example, while on the hints path, he attempted to calculate density in his head without using a calculator. After a few moments, he exclaimed, I am getting mixed up. I cant remember the numbers. He stared at the computer screen, clearly stumped. After a few moments, he started again and this time used the hints offered by the system, as well as a pen and paper to calculate.
During Unit 1, Oren did not use all the hints and tried to turn to the teacher when he encountered difficulties. When told that the teacher is not permitted to help, he was frustrated and guessed some answers. He did not turn to the web even though he stated at the beginning of the unit that he is highly skilled at seeking information on the web. During Unit 2, Oren was more reflective and worked slowly, double checking his answers. He also used the hints but still did not turn to the web. During the third unit, Oren used the hints path more often (3 out of 4 times) and often checked the web for information. These changes suggest progress in use of SRL behaviors.
Another example of Orens progress is his control of his impulsiveness (indicating strategic planning). In Unit 1, Oren was impulsive and did not always read the questions carefully and thoroughly. However, in the second and third units, Oren read all the information carefully before answering and was more cautious. He implemented this strategy openly and explicitly mentioned it. When asked for his opinion about the Te-SRL system, Oren said,
I think that the path choices make us think more about the problems. We have to make decisions so we need to take into account a lot of things like, do we know the content and skills, and do we think we can handle it.
Most of the time I like the fact that we have to rely on ourselves. There is no other choice. All we have is ourselves and the system. . .
I like the fact that different learners can use different paths. . . it also allows all the students to do well.
In these excerpts, Oren describes the system as a tool that elicits thinking and metacognition, promotes independent learning, and gives a heterogeneous variety of students a chance to succeed. Figure 4 summarizes the progress of Orens SRL strategies and performances during the three Te-SRL system units. The figure depicts both the change (or no change) over time regarding the seven strategies and features described previously and the performance on the Te-SRL units (last column).
Figure 4. Orens progress in relation to SRL features and performances on the three Te-SRL system units
Oren is an example of a student who, over time, developed all the observed SRL strategies and at the same time improved his performance on the Te-SRL system problem-solving tasks.
Beth is a low-achieving student in science. Data from the evaluation forms showed that her ability to self-evaluate her performance on assessment tasks and on the Te-SRL units fluctuated over time. The following excerpts indicate a lack of SRL strategy development during the three Te-SRL system units.
On the first Te-SRL unit, Beth approached the task with motivation to succeed. When she realized she could not turn to the teacher for help, she seemed discouraged and anxious and said, I cant do it without help. The teacher always helps me. She also helped me with the assessment task. The following statement indicates that she attributed her ability to succeed to the teacher, as opposed to herself: I need the teacher to help me; if she helps, I do much better.
An indication of her low SE in the face of the task was also apparent in her statements during the second and third units:
I tried last time and did not do so well. I guess I am not so good. I need a lot of help. (Unit 2)
If the teacher cant help me answer the questions, then there is no point&. I cant do it on my own. . . (Unit 3)
In the first problem of the first Te-SRL unit, Beth chose the hint path, which may reflect her feeling of a need for external help. When attempting to solve the first question, she took her time and appeared thoughtful. She expressed relief: This is not as hard as I thought it would be. After she felt that she had succeeded in answering the problem correctly, she decided to change paths and chose the direct path for the second problem. As soon as she read the first question, she expressed regret: This is much harder. How was I supposed to know it would get harder? While saying this, her body language indicated that she was discouraged.
Note that we told the students that the level of difficulty increases from problem to problem. This pattern repeated itself in Unit 3: Following a relatively successful problem-solving session, Beth opted out of the hints path instead of realizing that the hints helped her succeed. This trait of not learning correctly from experience manifested itself in various ways throughout the units. Although Beth verbally expressed her need for help multiple times and frequently chose the hints path, in actuality, she read very few of the hints. The following is a typical dialogue between the researcher and Beth during the units:
R: Why didnt you click on the hint icon to read the hint?
B: Oh. I dont know.
She only used the web when explicitly required to do so. This behavior repeated itself in the following two units.
Although she used some strategies to answer the questions, she was usually unsuccessful and did not always follow through, as shown in the following example: While Beth attempted to match the graph that corresponds to the data table, the researcher prompted her into verbalizing her thoughts:
R: What are you doing?
B: I have to answer this question (points at the screen).
R: What do you need to do?
B: I need to read the table, then compare it to the different graphs and choose the one that represents the data in the table.
R: How will you do that?
B: I will start with the first line in the table and compare it to the first dot on the graph. . .
Although Beth continued to describe the steps of her strategy, she implemented only the first step, skipping the rest, and making the wrong choice. This indication of a deficiency in strategy use continued in the following units.
Beths protocols and body language indicated that her affective state negatively affected her SRL progress. She approached Units 2 and 3 expressing a low feeling of SE and rarely verbalized her actions or thoughts without prompting. Her utterances expressed her feeling that she will not succeed in solving the problems correctly. Indeed, Beths performance on all three units was poor. When asked about her opinion concerning the TE-SRL system, Beth replied,
I like that there is a choice of paths; that way I can work without slowing everyone down and I also have a chance of answering correctly.
I like having hints. It makes me feel I can control what I know.
Figure 5 summarizes the progress of Beths SRL strategies and performances during the three Te-SRL system units.
Figure 5. Beths progress in relation to SRL features and her performances on the three Te-SRL system units.
Beth is an example of a student who did not take full advantage of the Te-SRL system and did not really use the scaffolding of the system. Her SRL skills and learning skills are poor, and she does not believe in her ability to succeed. As a result, her performance on the Te-SRL units is low, and she does not seem to benefit from the Te-SRL environment.
Adam is a medium-achieving student. Data collected from his self-evaluation forms indicate a lack of improvement of this skill over time. One aspect of Adams developing SRL skills during the three units was reflected in his increased use of learning and problem-solving strategies. In Unit 1, Adam seemed to drift through the problem-solving process without exerting much effort and without implementing learning and SRL strategies. He did not plan his learning or monitor his effort and did not use the help offered. When asked how he will find information, he answered, I dont know . . . I will start working, it will be O.K. In order to answer the first questionwhere is Lapland?he typed Lapland in the search engine and clicked on the first option in the index presented. He did not look at the whole web page and did not read more than one line. He hurried back to the index. He did not stop to plan how to find the information needed. He hurried to choose a path for solving the problems without reading important information about the task at hand and did not obtain the requisite knowledge and skills needed for successfully solving the problems. When asked why he chose the path, he answered, I dont know. I just clicked on the middle one. I usually choose the middle because I am also mediocre. These utterances may reflect a low sense of SE.
When Adam felt he did not answer the question successfully, he attributed this to external factors: The computer was not fast enough, the colors on the picture confused me, if you would have helped me I would have known the answer.
Although Adam chose the hints path for solving all the problems in the first unit, he did not actually read the hints. At one point during Unit 1, he discovered the advantage of using the hints, but he did not use them consistently thereafter. However, when he did use them, he changed his answers correctly. Adam did not seek information on the Internet even when he realized that he lacked the specific information needed to answer the questions: It is too bad that I cant remember the definition of this concept. Following this utterance, Adam stared at the screen and proceeded to guess the answer while uttering, I will just guess. . . The above description of Adams behavior in Unit 1 suggests a lack of planning, lack of use of learning strategies, and lack of effort.
However, in Unit 2, Adam changed his approach. He did relatively well on Unit 1 and therefore believed in his ability to succeed again, as reflected in his statement, OK, last time I did OK so I guess that shows I can do it. I feel that this time will be OK too. I think I learned how to use this system. His protocol and the body language used in Unit 2 suggest that Adam learned from experience, used prior knowledge, monitored his learning, and planned his choice of path according to his perception of his capabilities. An example is when he stopped to think before choosing a path and said OK. I am not sure I am so good with this subject. I did not do too well on the assessment so I will choose the hints paths. After solving the first problem, he said, I do not think I did so well. I think I should go back and read the information again. Later he paused and said, I will check on the Internet to make sure my answer was right.
At one point during Unit 2, Adam chose the direct path, stating, I want to see if I am capable of solving a problem on my own. I think I can do it. I want to try. This may indicate a growing sense of SE, thus allowing him to face obstacles. When facing difficulties, Adam tended to use the hints offered and used the web to seek information a couple of times. In Unit 3, Adam frequently read the hints and used the web to search for information and overcame his difficulties. It seems that Adam learned to navigate and utilize the Te-SRL system and developed specific self-regulating skills that assisted him during the problem-solving process.
When asked about his opinion concerning the Te-SRL system, Adam described the system as one that is interesting and that allows a heterogeneous variety of students to succeed; it boosts confidence and promotes learners self-regulation:
I liked the questions. They are different and challenging and are not boring.
It is more interesting. It is also good for students like me. I am an average student and when I ask questions, it annoys the good students, so I dont ask them. In this kind of activity, I can get help without bothering someone else. The good students dont have to feel that they are being annoyed or are getting bored because they can just not proceed by the help route. I also like that I can use information on the Internet. It gives me confidence. I also like that the questions were like stories and about things we usually dont learn in school. It makes it interesting. I like that I have to fend for myself and think alone. It frustrated me at first but now I like it.
Figure 6 summarizes the progress of Adams SRL strategies and performances during the three Te-SRL system units.
Figure 6. Adams progress in relation to SRL features and performances on the three Te-SRL system units
Adam exemplifies a student who seemed to learn how to take advantage of the Te-SRL system and the scaffolding offered and benefit from it. The data indicate that his SRL skills and learning skills improved over time, as did his SE beliefs. In fact, Adams performance on the Te-SRL units improved over time.
TRENDS OF PROGRESS IN STUDENTS SRL STRATEGIES AND PERFORMANCES
Figure 7 summarizes the progress of all 9 students SRL strategies and performances during the three Te-SRL system units.
Figure 7 Summary of the nine students SRL strategies and performances progress during the three Te-SRL system units
These findings from the observations and think-aloud protocols of 3 students (Oren, Beth and Adam) while using the Te-SRL system over time reflect those of the 9 students who participated in the research and highlight the following phenomena and trends:
Five students showed an improvement over time in their self-evaluation skills and performance on the Te-SRL system units. A statistical correlation between self-evaluation skills and performances on the Te-SRL system was found in the related quantitative research (Berglas-Shapiro, 2015).
Most students were aware of their knowledge concerning a given task and improved their ability to utilize prior knowledge when they made decisions in the Te-SRL system.
Students with initial average and high SE improved their SE throughout their experiences with the Te-SRL environment, whereas students with an initial low SE did not improve during that time.
A total of 4 of the 9 students improved their strategy of learning from experience throughout their interactions with the Te-SRL environment.
The use of the scaffolding in the Te-SRL system increased over time.
A total of 7 out of the 9 students did not use the Internet to seek information concerning their problem solving. It interesting to note that this finding reflects the results of the quantitative research, revealing that about 70% of the students did not use the Internet. Furthermore, A t test analysis of the quantitative data showed that the performance on the Te-SRL units of those students who did use the Internet to seek information (M = 87.02, SD = 9.4) was significantly higher than the performance of students who did not seek information on the web (M = 69.37, SD = 17.9), t(293) = -8.98, p < .0001 (see Berglas-Shapiro, 2015).
The strategic planning and use of learning strategy skills improved over time in 5 of the 9 students.
The performance on the Te-SRL units improved in 7 of the students.
One assumption of this study was that learning with TELEs requires students to have a unique set of skills for this purpose. Other learning skills, such as reading skills, information representation skills, and problem-solving skills, are needed to learn from TELEs, but these are not enough. Technologies do not merely support learning; they transform how we learn and how we interpret learning (Säljö, 2010). Research has shown that students who learn successfully with TELEs are those who, among other skills, have developed good self-regulating skills (e.g., Green et al., 2013). These skills include the ability to carry out metacognitive, motivational, and behavioral processes (Zimmerman, 2001, 2002).
Our Te-SRL provided a digital arena to observe students self-regulating actions and behaviors while solving problems. According to students responses to questions relating to the design of the Te-SRL, apparently students see an advantage in a system that offers different scaffolding for different learners and allows students to advance at their own pace and according to their own decisions. Despite this positive attitude, the case studies described earlier show that students do not always take advantage of the features offered; they do not always choose a path that is well suited to their abilities and knowledge; they do not use the hints and guidance offered to them; and they rarely use the web as a resource. However, this study also indicates that students can learn how to use the system to their advantage and develop the SRL strategies needed to do so. It is clear that learning science with the Te-SRL system contributed to the improvement of certain SRL features and strategies, among which are knowledge of cognition, SE, strategic planning and use of learning strategies, learning from experience, and utilizing scaffolding.
The findings indicate that not all students perform well in the Te-SRL system despite the support and the scaffolding offered in the Te-SRL environment. We attribute this to the propensity conditions of the research: the skills, aptitude, motivation, and knowledge that the students bring with them to the learning situation. For students to learn and perform successfully in the Te-SRL system, they need to develop effective SRL strategies, general learning skills, and ICT skills. The following are examples of propensity conditions in our study related to each of these categories of skills:
Self-evaluation skills: An example of an SRL skill that is needed for successful navigation and learning in the Te-SRL is students ability to evaluate their knowledge and mastery of skills. Students who lack this ability may have difficulty making proper choices.
Exertion of effort: Another example of students SRL skills is their exertion of effort when solving a problem. Students who are not confident in their capabilities are much less likely to try hard and are more likely to give up easily at the first sign of difficulty or try to get help to complete the task without learning or mastering it (see for example, Linnenbrink & Pintrich, 2010). One example of this is Oren, who in Unit 1 tried to elicit answers from the teacher and answered the question quickly and hastily. By the third unit, however, Orens behavior changed, and he exerted more effort to solve the problems, utilized the scaffolding and the web, and worked slowly and carefully. Concurrently, Orens performance on the Te-SRL units improved. Low exertion of effort may indicate low SE.
Learning and ICT skills: Seeking information on the web is one example of an ICT skill and a general learning skill. Many students in our study had difficulty finding the information they sought. It seems that although students are adept and use the Internet for gaming and for social purposes, they lack many important skills needed for intelligent searching of knowledge and for comprehensive reading of texts. These are important skills in the traditional learning environment as well as in the technological age and are pertinent for meaningful and independent learning. The findings of the study show that students ability to read and decipher texts, graphs, and tables affected their performance on the Te-SRL units.
Findings of this study reveal that many students do not make use of digital scaffolding offered to them and other sources of technologically enhanced assistance (e.g., the Internet) and instead either rely on themselves or on an external source of help (e.g., the teacher), or give up when facing difficulties. For students to solve problems independently and exert effort in the face of difficulties, they need to master the learning skills and strategies needed to do so. This study points to the importance of using TELEs as part of a learning environment that includes traditional learning, practicing, assessing contents and skills, as well as self-evaluation activities. The technological component should be part of a continuum and not be a stand-alone activity. In addition, when introducing a TELE activity to students, the teacher should instruct students how to work with the environment: navigation instruction and presentation of the system features (including icons, the scaffolding offered, and navigation strategies). Although it may seem that students are adept at working with computers, such assumptions may not be accurate; some students have difficulty transferring their skills from their daily life to learning situations. In addition, it is important to develop the culture of learning with technology-enhanced learning environments that offer different types of scaffolding. The development of such environments should provide the basic background knowledge (knowledge and skills); it should take into account the different students who learn with the TELE and their special needs; it should address the issue of making students aware of the environments attributes and how it contributes to their learning; and it should support the development of SRL skills such as self-evaluation skills.
The results of this study point to the benefit of explicitly teaching students how to evaluate their learning progress. The ability to self-evaluate achievements and performance accurately affects their students self-regulated learning and learning outcomes. Therefore, it seems prudent to allocate time and generate opportunities for self-evaluation in the classroom.
Aleven, V., Stahl, E., Schworm, S., Fischer, F., & Wallace, R. (2003). Help seeking and help design in interactive learning environments. Review of Educational Research, 73(3), 277320.
Alexander, R. J. (2002, April). Dichotomous pedagogies and the promise of comparative research. Paper presented at American Educational Research Association Annual Conference, New Orleans, LA.
Azevedo, R., & Aleven, V. (2013). International handbook of metacognition and learning technologies. New York, NY: Springer.
Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students? Journal of Educational Psychology, 96(3), 523535.
Azevedo, R., Verona, M. E., & Cromley, J. G. (2001). Fostering students collaborative problem solving with RiverWeb. In J. Moore, C. Redfield, & W. Johnson (Eds.), Artificial intelligence in education: AI-ED in the wired and wireless future (pp. 166175). Amsterdam, The Netherlands: IOS Press.
Bailey, A., & Heritage, M. (2014). The role of language learning progressions in improved instruction and assessment of English language learners. TESOL Quarterly, 48, 480506.
Barrie, S. C. (2007). A conceptual framework for the teaching and learning of generic graduate attributes. Studies in Higher Education, 32(4), 439–458.
Beetham, H., & Sharpe, R. (2013). Rethinking pedagogy for a digital age: Designing for 21st century learning. New York, NY: Routledge.
Beishuizen, J. (2008). Does a community of learners foster self‐regulated learning? Technology, Pedagogy and Education, 17(3), 183193.
Bennett, N., Dunne, E., & Carre, D. H. (1999). Patterns of core and generic skill provision in higher education. Higher Education, 37(1), 7193.
Berglas-Shapiro, T. (2015). Learning and instruction of science contents and representational skills in a technology-enhanced environment designed to support self-regulated learning (SRL) in junior high school (Unpublished doctoral dissertation). Weizmann Institute of Science, Rehovot, Israel.
Berglas-Shapiro, T., Eylon, B. S., & Scherz, Z. (2013). Explicit instruction of learning skills for science embedded in physics learning materials. In Proceedings of the Fifth International Conference on Education and New Learning Technologies (pp. 22532258) Valencia, Spain: IATED Publications.
Bernacki, M. L., Aguilar, A. C., & Byrnes, J. P. (2011). Self-regulated learning and technology-enhanced learning environments: an opportunity-propensity analysis. In G. Dettori & D. Persico (Eds.), Fostering self-regulated learning through ICT (pp. 126). Hershey, PA: Information Science Reference. doi:10.4018/978-1-61692-901-5.ch001
Brush, T., & Saye, J. (2001). The use of embedded scaffolds in a technology-enhanced student-centered learning activity. Journal of Educational Multimedia and Hypermedia, 10(4), 333356.
Bybee, R. W. (2014). NGSS and the next generation of science teachers. Journal of Science Teacher Education, 25(2), 211221.
Campbell, B., Kaunda, L., Allie, S., Buffler, A., & Lubben, F. (2000). The communication of laboratory investigations by university entrants. Journal of Research in Science Teaching, 37(8), 839853.
Carneiro, R., Steffens, K., & Underwood, J. (2005, September). Self-regulated learning in technology enhanced learning environments. Proceedings of the TACONET Conference in Lisbon. Aachen, Germany: Shaker.
Chipman, S. F., & Segal, J. W. (1985). Higher cognitive goals for education: An introduction. In J. W. Segal, S. F. Chipman, & R. Glaser (Eds.), Thinking and learning skills: Relating instruction to research (Vol. 1, pp. 121). Hillsdale, NJ: Erlbaum.
Dettori, G. (2008). Narrative learning environments. In A. Cartelli & M. Palma (Eds.), Encyclopedia of information communication technology (Vol. 2, pp. 576583). Hershey, PA: IGI Global.
Facer, K. (2011). Learning futures: Education, technology and social change. London, England: Routledge.
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906911.
Greene, J. A., Dellinger, K. R., Tüysüzoğlu, B., & Costa, L. (2013). A two-tiered approach to analyzing self-regulated learning data to inform the design of hypermedia learning environments. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 117128). New York, NY: Springer.
Greiff, S., Wüstenberg, S., Goetz, T., Vainikainen, M.-P., Hautamäki, J., & Bornstein, M. H. (2015). A longitudinal study of higher-order thinking skills: Working memory and fluid reasoning in childhood enhance complex problem solving in adolescence. Frontiers in Psychology, 6, 1060. http://doi.org/10.3389/fpsyg.2015.01060
Hacker, D. J. (1998). Metacognition: Definitions and empirical foundations. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 123). Mahwah, NJ: Erlbaum.
Hill, J. R., & Hannafin, M. J. (1997). Cognitive strategies and learning from the World Wide Web. Educational Technology Research and Development, 45(4), 3764.
Jonassen, D. H., & Reeves, T. C. (1996). Learning with technology: Using computers as cognitive tools. In D. Jonassen (Ed.), Handbook of research on educational communications and technology (pp. 693719). New York, NY: Macmillan.
Lajoie, S. P. (1993). Computer environments as cognitive tools for enhancing learning. In S.P. Lajoie & S. Derry (Eds.), Computers as cognitive tools (pp. 261288). Hillsdale, NJ: Erlbaum.
Land, S. M. (2000). Cognitive requirements for learning with open-ended learning environments. Educational Technology Research and Development, 48(3), 6178.
Land, S., & Greene, B. (2000). Project-based learning with the World Wide Web: A qualitative study of resource integration. Educational Technology Research and Development, 48(3), 6178.
Linnenbrink, E., & Pintrich, P. (2010). The role of self-efficacy beliefs in student engagement and learning in the classroom. Reading & Writing Quarterly, 19(2), 119137. doi:10.1080/10573560308223
National Mathematics Advisory Panel, U.S. Department of Education. (2008). The Final Report of the National Mathematics Advisory Panel. Retrieved from http://www2.ed.gov/about/bdscomm/list/mathpanel/report/final-report.pdf
National Research Council Committee on a Conceptual Framework for New K12 Science Education Standards. (2012). A framework for K12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academy Press. Retrieved from http://www.cslnet.org/wp-content/uploads/2013/07/Next-Generation-Science-Framework-Final-Report.pdf
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451502). San Diego, CA: Academic Press.
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667686.
Pintrich, P. R., Smith, D., Garcia, T., & McKeachie, W. (1993). Predictive validity and reliability of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational Psychology, 53, 801813.
Säljö, R. (2010). Digital tools and challenges to institutional traditions of learning: Technologies, social memory and the performative nature of learning. Journal of Computer Assisted Learning, 26, 5364.
Scherz, Z., Michman, M., & Tamir, P. (1986). Attitudes of university candidates towards learning activities aimed at preparation for science studies. Research in Science & Technological Education, 4(2), 183194.
Scherz, Z., Spektor-Levy, O., & Eylon, B. (2005). Scientific Communication: An instructional program for high-order learning skills and its impact on students performance. In K. Boersma, M. Goedhart, O. de-Jong & H. Eijkelhof (Eds.), Research and the quality of science education (pp. 231243). Dordrecht, The Netherlands: Springer. doi:10.1007/1-4020-3673-6
Schunk, D. (2001). Self-regulation through goal setting. ERIC/CASS digest. Retrieved from http://files.eric.ed.gov/fulltext/ED462671.pdf
Schunk, D., & Swartz, C. (1993). Goals and progress feedback: Effects on self-efficacy and writing achievement. Contemporary Educational Psychology, 18(3), 337354.
Silva, E. (2009). Measuring skills for 21st-century learning. Phi Delta Kappan, 90(9), 630634.
Someren, M., Barnard, Y., & Sandberg, J. (1994). The think-aloud method. A practical guide to modeling cognitive processes. London, England: Academic Press.
Spektor-Levy, O., Eylon, B., & Scherz, Z. (2009). Teaching scientific communication skills in science studies: Does it make a difference? International Journal of Science and Mathematics Education, 7(5), 875903. doi:10.1007/s10763-009-9150-6
Steffens, K. (2006). Self-regulated learning in technology-enhanced learning environments: Lessons of a European peer review. European Journal of Education, 41(34), 353379.
Stone, E. (2014). Guiding students to develop an understanding of scientific inquiry: A science skills approach to instruction and assessment. Cell Biology Education, 13(1), 90101.
Straka, G. A., Nenniger, P., Spevacek, G., & Wosnitza, M. (1996). A model for motivated self-directed learning. Education, 53, 1930.
Trilling, B., & Fadel, C. (2009). 21st century skills: Learning for life in our times. San Francisco, CA: Jossey-Bass.
Wandera, D. B. (2014). The threat of obsolescence: Teaching and learning responding to technology. Technology, Pedagogy and Education, 24(2), 279281.
Waring, M., & Evans, C. (2015). Understanding pedagogy: Developing a critical approach to teaching and learning. New York, NY: Routledge.
Way, J., & Rowe, L. (2008, July). The role of scaffolding in learning design of multimedia learning objects. Paper presented at 11th International Congress on Mathematics Education, Monterrey, Mexico.
Weinstein, C. E., Schulte, A., & Palmer, D. R. (1987). The learning and study strategies inventory. Clearwater, FL: H & H Publishing.
White, B., Shimoda, T., & Frederiksen, J. (2000). Enabling students to construct theories of collaborative inquiry and reflective learning with SCI-WISE: An approach to facilitating meta-cognitive development. In S. Lajoie (Ed.), Computers as cognitive tools, Volume Two: No more walls (pp. 97132). Mahwah, NJ: Erlbaum.
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25, 317.
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of selfregulation: Theory research, and applications (pp. 1329). San Diego, CA: Academic Press.
Zimmerman, B. J. (2001). Theories of self-regulated learning and academic achievement: An overview and analysis. In B. Zimmerman & D. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (pp. 139). Mahwah, NJ: Erlbaum.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 6472.