chapters 15,16

The Worked-Out Examples Principle in Multimedia Learning

CHAPTER 15

Emtinan Alqurashi

Alexander Renkl

Sep 30, 2014

Worked-out examples?

Definition

A step-by-step demonstration of how to perform a task or how to solve a problem.

(More than one example)

Problem formulation

What is learning from worked-out examples?

Final solution

Solution steps

The worked-out principle

Preferred by learners

Leads to superior learning outcomes

Deep understanding

(if implemented according to specific guidelines)

Compare.

A worked-out example

Split-attention format Integrated format

Guidelines for example design

Self-Explanation Guideline

Help Guideline

The easy-mapping guideline

The structure-emphasizing guideline

The meaningful building-blocks guideline

Mark:

As we learned in our study of the split-attention principle, learners often experience diminished results often due to the overwhelming cognitive load created when requiring learners to integrate unintelligible visual and text materials in multimedia instruction. According to Tarmizi and Sweller (1988), "productive self-explanations were hindered". Given our growing understanding of the cognitive architecture and cognitive load theory, it stands to reason that making explanations easier through very relevant, integrated representations will reduce cognitive load, thereby allowing for greater learning to occur. I found it interesting that dual modality re-emerged as another factor to increase learning within the easy-mapping guidelines, though Jeung, Chandler and Sweller (1997) qualified earlier research by Mousavi, Low and Sweller (1995), identifying that aural explanations of visually complex, unfamiliar material had diminishing returns. Finally, the complexity of managing all of the principles within the scope of the easy-mapping guideline is punctuated with the statement "there is no definitive empirical answer to this question" [of when to use dual modality or signalling].

Marcia:

According to Renkle (2005), the deep understanding achieved from a worked-out example is seen “only if the example-based learning is well-designed” (p. 234). Well-designed implies many things, but I want to focus on the meaningful building blocks guidelines. When individuals are solving problems, the worked out examples can highlight the various “subgoals” through signaling. This allows learners to solve new problems and think of the individual parts of the worked-out example as separate, not necessarily a process that must be done all together. The studies of Catrambone (1995, 1996, 1998), have shown that the highlighting of subgoals leads to learning and self-explanations that create better understanding of the individual steps.

SELF-EXPLANATORY

Study by Chi, Bassok, Lewis, Reimann, and Glaster (1989)

Renkl (1997)

Self-explanatory activities

1. Principle-based explanations

2. Explications of goal-operator combinations

3. Example comparison

4. Anticipative reasoning

Implications of the research for cognitive theory on Learning and Instruction

Problem solving & learning are not always two sides of a coin.

Emphasizing the importance of guided constructive activity.

Cognitive load is an important aspect to be considered in learning.

LimitationsFocusing on only one solution

No exploitation of error-trigged learning

Relevant only to a limited range of domains

Evidence primarily from experimental settings of limited ecological validity

Focusing on only one solutionNola: One of the limitations of learning from worked-examples is the Focusing on Only One Solution restriction which is as stated by Renkl (2005) that "traditional worked out examples show just one solution procedure although in many cases multiple solutions are possible."

Math Problem7x7?

7x2= 14 (2 sevens) +14 (2 sevens) ______ 28 +14 (2 sevens) ______ 42 + 7 (1 seven) ______ = 7 sevens 49

answer: 7x7 =49

No exploitation of error-trigged learningChunhua: VanLehn (1999) emphasize that errors are triggers for reflection that deepen understanding. For many situations in our training meeting, we use some errors to enhance the outcomes of learning as well. For example, we presented some examples to show the certain function of our service, usually, for comparing the correct examples, we would show the audience one or two faulty solutions to deepen their impression. I think that the faulty examples that did not be used in the design of mutlimedia learning should be deficit and we surely can pay more attention on it.

Limitations

Focusing on only one solution





Elif: Worked-out examples include both the solution and steps toward reaching the solution. But, solution steps can only be provided in a limited number of domains such as mathematics, physics, computer programming, etc. Fortunately, recent studies have shown that this worked-out problems can be used in nonalgorithmic domains. Altough this restriction is remarkable, al least the researchers started to extend the range of domains that worked-out examples can be used.

Relevant only to a limited range of domainsJingwei: Work-out example learning is skill-approach learning method. Hence, the most effective domain fall into the skill based algorithms subjects. Even though the domain range is getting broaden, the mature example-based learning still in the algorithms domain. Since the worked-out example "consist of a problem formulation, solution steps and the final solution." (Renkl, 2003). The components are more suitable for the algorithms while social studies or arts do not have certain logical procedure. If we google "how to write a good research paper", we may have a list of how to do this which including a good literature review, a good methodology. But they are not that accuracy to reach the final solution - to write a really good paper. And also the standard of "a good paper" may vary. Hence, the restriction is obvious and can not be solved only for broaden the domain range, but also to standardlize the procedure of the domain which need to use example-based learning method.

Rachel: The potential of worked examples is very high for learning, however, the circumstances must be right. One thing that can hinder the circumstances are if the examples are processed superficially. This occurs when the learner does not construct “knowledge structures” that are relevant to the domain. This can happen due to two reasons: the learner has processing deficiencies with the content or the worked-example’s characteristics are suboptimal. The “suboptimal” examples can occur if they cause extraneous load or fail to “invite” generative processing.

I will focus on the former limitation, learner deficiencies. One thing that can contribute to a learner having “suboptimal processing” is due to a lack of prior knowledge. Berfthold and Renkl (2009) write that to overcome this, it “makes sense to help out by providing instructional explanations.” Getting everyone’s prior knowledge on the same “playing field” seems to be one of the keys to avoiding suboptimal processing.

The Collaboration Principle in Multimedia Learning

CHAPTER 16

Emtinan Alqurashi

Marguerite RoyMichelene T. H. Chi

Computer Supported Collaborative Learning (CSCL)

Research-based principles for collaboration

7 issues related to CSCL systems:Nature of technology usedNature of the group compositionNature of the task engaging learnersRole of students and instructors Process of community buildingsNature of assessments Scaffolding collaboration

The Nature of technology used

Online communication tools

Student outcomes

Web-based communication

The Nature of the group composition

Gender

Group size

Learners characteristic

Mark: There appear to be some conflicting research findings with regard to online gender numbers, with Bisciaglia and Monk-Turner (2001) indicating female majority participation, versus Orly et al (2001) who concluded the opposite. Setting aside the matter of accuracy in determining gender participation, it would appear one of the key differences would be females engendered a sense of community more than their male counterparts. "Additionally, female students exhibited a mostly connected communication pattern whie the communication of males was mostly independent" (Rovai, 2001). Drawing upon the asynchronous course I am taking, (and recognizing that the sample and group data I use by no means constitutes a scientific study), this would hold up. There is more connectedness and community amongst the females in the group than exhibited by the males. The sense of obligation to interact and contribute also seems diminished among the males in the group. Outside of the gender factor, it's interesting that the learning characteristic factors also mention females as performing higher than low-learning oriented students (and by omission, males).

Jingwei: The key factor of online learning is collaboration. To build an effective online learning, one research has drew an attention to how demographic chracteristics affect CSCL. The research (Tung 2010) result showed that female has higher perceptions than male, native language speaking students are more engaged into the CSCL, and instructors had statistically significant higher perceptions towards online course than students in collaboration. Female is more willing to participant in the online discussion and share their opinion, and small group size can envolve most students into the discusstion and course project. The learner characteristic I think is the most crucial factor, since it control the nature of how students perform in online collaboration. More engagement more effective learning outcomes can be established.


Gender

Group size


Marcia: “CSCL is based, not surprisingly, on the idea that knowledge is socially constructed within a community. One aspect of group composition is group size. Interesting, though, that Jonassen et al (2005) do not really discuss group size in very specific manner, instead focusing on the studies that show that working in groups often leads to better performance (Jessup, Egbert, & Connolly, 1995; Uribe, Klein, & Sullivan, 2003). While the best group size most likely depends on the context (Scanlon et al., 1997), it would be interesting to discover an upper limit to group size. This could be useful information for MOOCs so that students could be divided into groups that are not overwhelming. Considering that in the Jonassen (2005) study, groups that interacted the most "felt more interrupted and more hurried" (p. 253), having a group with many members all trying to interact could be counterproductive”.

Chunhua: For online collaborative learning environments, the group size should be considered more carefully.

As Uribe, Klein, and Sullivan (2003) investigated, the participants who worked in computer-mediated collaboration on solving ill-defined problems benefited and indicated a preference for working collaboratively. This confirmed, for certain circumstance or task, that the participants who worked in group performed better than participants who worked alone. Moreover, Scanlon et al. (1997) reported that when group size rose above four that group performance diminished for participants using an audio and video synchronous collaboration tool. That means the design of group size need to depend on technology as well, and I believe that more studies and practice about this point is needed along with the emerging new technology.


Gender

Group size


Nola: Learner characteristics influencing "online collaborative learning environments provide new opportunities to compare different and more varied groups and these these differences may contribute to the richness and interactivity of the learning (Jonassen, Lee, Yang & Laffey, 2005)."

For example, in an online statistics course an instructor might give a survey the first week of class asking the students how comfortable they are with their knowledge of statistics. The levels would range from 'very comfortable', 'somewhat comfortable' to 'not very comfortable'. Then the instructor would divide the groups into 4 per group with varying levels of comfort with statistics within each group. The students would use on online discussion board to collaborate on statistic problems. As stated above " these differences may contribute to the richness and interactivity of learning". Those with a higher level of comfort would be able to assist those with lower levels of comfort with statistics. Those with lower levels of comfort may have more questions about the statistics problems. This would lead to more interactivity between the group members as they discuss the material.

Elif: In chapter 16, besides gender and group size, learner social, cognitive, and cultural characteristics have been investigated to explain how they influence group activity and online learning. Kirkley et al. (1998) found that "in higher education online course U.S student sent more messages than Asian students". Oppositely, Pilkington and Walker (2003) reported that in a virtual learning environment, "non-native speakers make more use of the communication tools than native students". A few factors may cause this conflict in research: expertise level of students or their culture.

Rachel: There wasn’t as much focus on gender, group size, and learner characteristic in my chapter that I think relates to your chapters. This chapter was all about group collaboration and what is important characteristics to ensure the collaboration is effective and appropriate. Kirschner, Kirchner and Janssen write of three things that make collobortion in multimedia learning effective: “1. Learning task is cognitively demanding enough to require collaboration…” “2. Cognitive processes and information necessary for learning are effectively and efficiently shared among group members; and 3. [the] multimedia environment provides the necessary tools for effective need efficient communication about the task, content… and regulation of [the] processes involved in carrying out the tasks.”

I will discuss the first necessity in effective collaboration, the learning task is cognitively demanding enough to require collaboration. In 2010, Rajaram and Pereir-Pasarin did a study on memory and collaboration. One would assume that memory-oriented tasks are not “cognitively demanding” and the results showed that those that studied the lists individually did better than those that discussed and collaborating about the lists. However, another study was conducted by Laughlin, Bonner, and Miner in 2002. The task was more demanding, problem solving, and the results were the opposite of the previous study. This suggests that the difficulty of the task plays an important role.

The Nature of the task engaging learners

Case studies

Debates

Problem solving

The Role of tutors

What are the characteristics of a good tutor?

The Role of tutors

Knowledge of instructional design

Planning skills

Content knowledge

Enthusiasm and expertise

The Process of community buildings

Social interaction

Learning and community

The Nature of the learning and communication assessment

Essays

Concept mapping

Examinations

Scaffolding Collaboration

chapters 15,16

Education