The double-edged effects of explanation prompts

Explanation prompts usually foster conceptual understanding. However, it has been claimed within cognitive load theory that prompts can take cognitive load to the upper limit when learning complex contents. Under such circumstances, prompts focusing the learners’ attention on specific aspects (e.g., conceptual aspects such as elaborations on domain principles) might have some costs: Other important aspects (e.g., procedural aspects such as how to calculate) cannot be processed deeply. Thus, we expected that conceptually-oriented explanation prompts would foster the detailedness of explanations, the number of elaborations on domain principles, and conceptual knowledge. In addition, we tested the influence of such prompts on the number of calculations performed during learning and procedural knowledge. We conducted an experiment in which we employed conceptually-oriented explanation prompts in a complex e-learning module on tax law. Tax law university students (N = 40) worked on this e-learning module under two conditions: (a) conceptually-oriented explanation prompts, (b) no prompts. The prompts led to double-edged effects: positive effects on the detailedness of explanations and on the number of elaborations on domain principles, as well as on conceptual knowledge and simultaneously negative effects on the number of calculations performed during learning as well as on procedural knowledge.     

The impact of goal specificity and goal type on learning outcome and cognitive load

Two hundred and thirty three 15-year old students conducted experiments within a computer-based learning environment. They were provided with different goals according to an experimental 2 × 2 design with goal specificity (nonspecific goals versus specific goals) and goal type (problem solving goals versus learning goals) as factors. We replicated the findings of other researchers that nonspecific problem solving goals lead to lower cognitive load and better learning than specific problem solving goals. For learning goals, however, we observed this goal specificity effect only on cognitive load but not on learning outcome. Results indicate that the goal specificity affects the element interactivity of a task and cognitive load with both, problem solving goals or learning goals. But differences in overall cognitive load are not sufficient for explaining differences in learning outcome. Additionally, differences in strategy use come into play. Specific problem solving goals seem to restrict students to use a problem solving strategy whereas nonspecific problem solving goals or learning goals allow students to use a learning strategy. We conclude that in order to foster learning, students must be provided with goals that allow them to use a learning strategy. Additionally, providing them with nonspecific goals decreases cognitive load and, thus, enables students to learn with less effort.     

Cognitive load in hypermedia reading comprehension: Influence of text type and linearity

In this paper the assumption of cognitive overhead in hypermedia learning is specified by cognitive load theory. This analysis is based on different types of cognitive load, the dimension of linearity/non-linearity as well as text characteristics. We propose a model stating that extraneous cognitive load in hypermedia learning is basically determined by the interaction of text presentation format (linear/non-linear) with text type (text with and without narrative structures). This assumption was tested by means of a 2 × 2 experimental design. Sixty participants completed a computer-based learning program that contained a narrative text or an encyclopaedia text in either linear or non-linear presentation format. Results confirm the suggested interaction hypothesis postulating that non-linear information presentation of narrative text structure increases cognitive load and decreases knowledge acquisition. However, for encyclopaedia text participants’ knowledge acquisition was not affected by linear or non-linear presentation format. Furthermore, results suggest a cross-validation of cognitive load measures and propositional analysis.     

The effects of time-compressed instruction and redundancy on learning and learners’ perceptions of cognitive load

Can increasing the speed of audio narration in multimedia instruction decrease training time and still maintain learning? The purpose of this study was to examine the effects of time-compressed instruction and redundancy on learning and learners’ perceptions of cognitive load. 154 university students were placed into conditions that consisted of time-compression (0%, 25%, or 50%) and redundancy (redundant text and narration or narration only). Participants were presented with multimedia instruction on the human heart and its parts then given factual and problem solving knowledge tests, a cognitive load measure, and a review behavior (back and replay buttons) measure. Results of the study indicated that participants who were presented 0% and 25% compression obtained similar scores on both the factual and problem solving measures. Additionally, they indicated similar levels of cognitive load. Participants who were presented redundant instruction were not able to perform as well as participants presented non-redundant instruction.     

Cognitive load and instructionally supported learning with provided and learner-generated visualizations

This study investigated, whether learning from science texts can be enhanced by providing learners with different forms of visualizations (pictures) in addition to text. One-hundred-two 9th and 10th graders read a computer-based text on chemical processes of washing and answered questions on cognitive load (mental effort, perceived difficulty) and comprehension (retention, transfer, drawing). Instruction varied according to a 2 × 2-factorial design with ‘learner-generated pictures’ (yes, no) and ‘provided pictures’ (yes, no) as factors. Results indicate positive main effects of provided pictures on all three comprehension measures and negative main effects on both cognitive load measures. Additional analyses revealed a mediation effect of perceived difficulty on retention and transfer, that is learning with provided pictures decreased cognitive load and enhanced comprehension. Furthermore, results show a positive main effect of learner-generated pictures on drawing and mental effort, but no mediation effect. Taken together, computer-based learning with provided pictures enhances comprehension as it seems to promote active processing while reducing extraneous cognitive processing. Learners, generating pictures, however, seem to have less cognitive resources available for essential and generative processing, resulting in reduced comprehension. These results are in line with cognitive load theory, cognitive theories of multimedia learning, and generative theories of learning.     

The effects of video on cognitive load and social presence in multimedia-learning

Two studies examined the use of video in multimedia learning environments. In Study 1, participants (N = 26) viewed one of two versions of a computer-based multimedia presentation: video, which included a video of a lecture with synchronized slides, or no video, which included the slides but only an audio narration of the lecture. Learning, cognitive load and social presence were assessed, but a significant difference was found only for cognitive load, with video experiencing greater cognitive load, t (24) = 2.45, p < .05. In Study 2, students (N = 25) were randomly assigned to either video or no video condition. Background knowledge and visual/verbal learning preference were assessed before viewing the presentation, and learning, cognitive load, and social presence were assessed after viewing. No significant differences were found for learning or social presence. However, a significant visual/verbal learning preference by condition interaction was found for cognitive load, F (1,21) = 4.51, p < .05: low visual-preference students experienced greater cognitive load in the video condition, while high visual-preference students experienced greater cognitive load in the no video condition.     

A cognitivist model for decision support: COGITA project, a problem formulation assistant

The formulation of a problem may be defined as a process of acquisition and organization of knowledge related to a given situation, on which a decision maker projects some action. The assistance in the problem formulation that we may expect within decision support systems is difficult to design and to implement. This is mainly due to the frequent lack of attention to a sufficiently formalized conceptual framework which would consider the decision with a more cognition sciences oriented approach. In the first part, we will present an instrumental model for the study of decision processes as an attempt to simulate the cognitive process of knowledge acquisition and organization carried out by a decision maker facing a problematic situation. Considering its epistemological foundations, this model can be named “cognitivist model”. Within this model, the decision is defined as a cognitive construction which we call “decisional construct”. It consists of the elaboration of one or several abstract representations of the problematic situation (formulation phase), and the design of operational models (solving phase). In the second part, we will present the COGITA project, which consists of the design and realization of an environment for the development of problem formulation assistance systems. The modelization and simulation of cognitive processes call for relevant techniques originating either in artificial intelligence or in connectionism. We will show which are the main characteristics, potentials, limits and complementarity of these techniques and why their integration is fundamental and necessary to the simulation of the cognitive process associated with the formulation. COGITA is a hybrid system currently under development which tends to integrate symbolic artificial intelligence techniques and connectionist models in a cooperative hybridation the general architecture of which is presented.     

Cognitive load theory vs. constructivist approaches: which best leads to efficient,deep learning?

Computer‐assisted learning, in the form of simulation‐based training, is heavily focused upon by the military. Because computer‐based learning offers highly portable, reusable, and cost‐efficient training options, the military has dedicated significant resources to the investigation of instructional strategies that improve learning efficiency within this environment. In order to identify efficient instructional strategies, this paper investigates the two major learning theories that dominate the recent literature on optimizing knowledge acquisition: cognitive load theory (CLT) and constructivism. According to CLT, instructional guidance that promotes efficient learning is most beneficial. Constructivist approaches, in contrast, emphasize the importance of developing a conceptual understanding of the learning material. Supporters of these theories have debated the merits and shortcomings of both positions. However, in the absence of consensus, instructional designers lack a well‐defined model for training complex skills in a rapid, efficient manner. The current study investigates the relative utility of CLT and constructivist‐based approaches for teaching complex skills using a military command and control task. Findings suggest that the acquisition of procedural, declarative, and conceptual knowledge, as well as decision‐making skills, did not differ as a function of the type of instruction used. However, integrated knowledge was slightly better retained by the group provided with CLT‐based instruction. These results are contrary to our expectation that constructivist approaches, which focus on the development and integration of information, would yield better performance in an applied problem‐based environment. Thus, while contemporary researchers continue to defend the use of constructivist strategies for teaching, our research supports earlier findings that question the utility, efficiency, and impact of these strategies in applied domains.     

Learning with intelligent tutors and worked examples: selecting learning activities adaptively leads to better learning outcomes than a fixed curriculum

The main learning activity provided by intelligent tutoring systems is problem solving, although several recent projects investigated the effectiveness of combining problem solving with worked examples. Previous research has shown that learning from examples is an effective learning strategy, especially for novice learners. A worked example provides step-by-step explanations of how a problem is solved. Many studies have compared learning from examples to unsupported problem solving, and suggested presenting worked examples to students in the initial stages of learning, followed by problem solving once students have acquired enough knowledge. This paper presents a study in which we compare a fixed sequence of alternating worked examples and tutored problem solving with a strategy that adapts learning tasks to students’ needs. The adaptive strategy determines the type of the task (a worked example, a faded example or a problem to be solved) based on how much assistance the student received on the previous problem. The results show that students in the adaptive condition learnt significantly more than their peers who were presented with a fixed sequence of worked examples and problem solving. Novices from the adaptive condition learnt faster than novices from the control group, while the advanced students from the adaptive condition learnt more than their peers from the control group.     

Cognitive load and science text comprehension: Effects of drawing and mentally imagining text content

One hundred and eleven 10th graders read an expository science text on the dipole character of water molecules (ca. 1600 words). Reading instruction was varied according to a 2 × 2 experimental design with factors ‘drawing pictures of text content on paper’ (yes, no) and ‘mentally imagining text content while reading’ (yes, no). The results indicate that drawing pictures, mediated through increased cognitive load, decreased text comprehension and, thus, learning (d = −0.37), whereas mental imagery, although decreasing cognitive load, increased comprehension only when students did not have to draw pictures simultaneously (d = 0.72). No evidence was found that the effects were moderated by domain-specific prior knowledge, verbal ability, or spatial ability. The results are in line with cognitive theories of multimedia learning, self-regulated learning, and mental imagery as well as conceptions of science learning that focus on promoting mental model construction by actively visualizing the content to be learned. Constructing mental images seems to reduce cognitive load and to increase comprehension and learning outcome when the mental visualization processes are not disturbed by externally drawing pictures on paper, whereas drawing pictures seems to increase cognitive load resulting in reduced comprehension and learning outcome.     

The effects of Logo and CAI problem-solving environments on problem-solving abilities and mathematics achievement

The purpose of the present study was to investigate the effects of Logo programming and CAI problem-solving software on problem solving that is dependent on specialized conceptual and procedural knowledge, problem solving that is dependent on specific executive-level cognitive skills, and mathematics achievement. No significant differences were found on mathematics achievement or knowledge-dependent problem solving. However, significant differences were found on the test of executive-level problem solving, with the Logo group improving more than the CAI and control groups.     

A taxonomy for computer-based assessment of problem solving

Computer-based assessment of problem solving is motivated by the need for educational assessments that are valid and efficient. Based on a recent revision of Bloom's taxonomy (Anderson et al., 2001, A taxonomy for learning, teaching, and assessing: a revision of Bloom's taxonomy of educational objectives. New York: Longman), assessment items should require applying a particular cognitive process to a particular type of knowledge. There are 19 types of cognitive processes that can be classified into six major categories: remember, understand, apply, analyze, evaluate, and create. There are four major categories of knowledge: factual, conceptual, procedural, and metacognitive. Examples of computer-based assessments of problem-solving are provided based on the evaluation of the cognitive consequences of children's participation in an after-school computer club.     

Cognitive engineering of a new telephone operator workstation using COGNET

Many cognitive engineering methodologies for user-centered design involve modeling procedural knowledge; others deal with domain semantics or conceptual models. COGnitive NEwork of Tasks (COGNET) is a framework for modeling human cognition and decision-making which provides an integrated representation of the knowledge, behavioral actions, strategies and problem solving skills used in a domain or task situation, yielding a powerful cognitive engineering tool. A case study of the design of the user interface for a new telephone operator workstation is presented to illustrate the derivation of the design from the components of the COGNET model. The model does not directly convey any specific feature of the interface design, but rather a formal representation of what the user must do with the resulting interface. This information is then evolved through a set of transformations which systematically move toward design features, in a fully traceable manner.

Relevance to industry

With the increasing prevalence of technical systems in complex work domains, cognitive engineering is necessary in designing the user interface for those systems to promote efficient integration of person and machine. The cognitive engineering methodology presented here addresses that need.     

Instructional designs for the development of transferable knowledge and skills: A cognitive load perspective

This paper analyzes the main points and results of a set of the previous papers in this Special Issue from the point of view of developing characteristics of flexible—transferable—expertise. It focuses on cognitive load issues related to the acquisition of deep transferable knowledge structures and developing metacognitive and self-regulation skills. The contributions to this Special Issue demonstrate that appropriate instructional support and optimal levels of control over the learning processes, enhanced by self-explanation and self-visualization techniques, may enhance learners’ abilities to transfer their knowledge and skills. Better understanding of the role of germane cognitive load, as well as our abilities to measure different types of load and high-level cognitive processes are essential for further progress in this area.     

Are two heads always better than one? Differential effects of collaboration on students�� computer-supported learning in mathematics

While some studies found positive effects of collaboration on student learning in mathematics, others found none or even negative effects. This study evaluates whether the varying impact of collaboration can be explained by differences in the type of knowledge that is promoted by the instruction. If the instructional material requires students to reason with mathematical concepts, collaboration may increase students’ learning outcome as it promotes mutual elaboration. If, however, the instructional material is focused on practicing procedures, collaboration may result in task distribution and thus reduce practice opportunities necessary for procedural skill fluency. To evaluate differential influences of collaboration, we compared four conditions: individual vs. collaborative learning with conceptual instructional material, and individual vs. collaborative learning with procedural instructional material. The instruction was computer-supported and provided adaptive feedback. We analyzed the effect of the conditions on several levels: Logfiles of students’ problem-solving actions and video-recordings enabled a detailed analysis of performance and learning processes during instruction. In addition, a post-test assessed individual knowledge acquisition. We found that collaboration improved performance during the learning phase in both the conceptual and the procedural condition; however, conceptual and procedural material had a differential effect on the quality of student collaboration: Conceptual material promoted mutual elaboration; procedural material promoted task distribution and ineffective learning behaviors. Consequently, collaboration positively influenced conceptual knowledge acquisition, while no positive effect on procedural knowledge acquisition was found. We discuss limitations of our study, address methodological implications, and suggest practical implications for the school context.     

What does it mean? Students’ procedural and conceptual problem solving in a CSCL environment designed within the field of science education

This article discusses the relationship between procedural and conceptual problem solving in a computer-supported collaborative learning (CSCL) environment designed within the field of science education. The contribution of this article, and our understanding of this phenomenon, is anchored in our socio-cultural interpretation, and that implies distinctive inputs for the design and re-design of these kinds of learning environments. We discuss institutional aspects linked to the school as a curriculum deliverer, as well as to the presentation of the knowledge domain and the construction of the CSCL environment. The data is gathered from a design experiment in a science setting in a secondary school, and video data is used to perform an interaction analysis. More specifically, we follow a group of four secondary school students who solve a biological problem in a computer-based 3D model supported by a website. Our findings are clear in the sense that the procedural types of problem solving tend to dominate the students’ interactions, while conceptual knowledge construction is only present where it is strictly necessary to carry out the problem solving. Based on our analyses, we conclude that this can be explained partly by how the knowledge domain is presented and how the CSCL environment is designed, but that the main reason is linked to the institutional aspects related to the school as curriculum deliverer where its target is to secure that the students actually solve problems that are predefined in the syllabus list. We argue that this affords some particular challenges, linked to making conceptual knowledge constructions in science education explicit in the CSCL environment, and to encouraging the teachers and the school as a curriculum deliverer to give this kind of knowledge construction a prioritised value.     

An instructional strategy planning model to improve learning and cognition

Developments in cognitive science (e.g., psychology, artificial intelligence, and expert systems) provide a framework to propose an instructional strategy planning model that links knowledge acquisition and employment with specific instructional strategies. The model presented in this article identifies unique computer-based instructional strategies to improve both learning and cognition. Using a meta-learning theory, the storage memory system components of declarative, procedural, and conceptual knowledge are respectively linked to drill and practice, tutorial, and task-oriented simulation strategies. Likewise, for the retrieval memory system components (i.e., differentiation, integration, and creation), the instructional strategies include problem-oriented simulations and self-directed experiences. Each of the instructional strategies are composed of variables and conditions that have been empirically tested and shown to improve specific forms of knowledge acquisition and employment.     

Computer literacy and inquiry learning: when geeks learn less

Abstract A low level of computer literacy has often been hypothesized as constituting a disadvantage in knowledge acquisition. However, within the field of computer‐supported inquiry learning systematic investigations of these purported relations have not been conducted. This classroom study investigates the role of computer literacy (procedural computer‐related knowledge, self‐confidence in using the computer, and familiarity with computers) as a learning prerequisite for knowledge acquisition, and analyses the learners' patterns of media use as processes that might explain this role. Thirty‐seven students from two final classes of a secondary school worked in pairs on the project ‘How far does light go?’ in the Web‐based Inquiry Science Environment. Findings did indicate significant relations of neither procedural computer‐related knowledge nor self‐confidence in using the computer to knowledge acquisition. However, students with greater familiarity with computers acquired significantly less knowledge. In the light of the patterns of media use, these findings might be explained by different navigation styles adopted by students with high and low familiarity with computers: students with high familiarity with computers exhibit more shallow processing strategies (‘browsing’) which are less functional for learning.     

Promoting college students’ knowledge acquisition and ill-structured problem solving: Web-based integration and procedure prompts

This study examined how web-based integration and procedure question prompts differentially affected students’ knowledge acquisition and ill-structured problem solving skills, particularly in representing problem(s), developing solutions, and monitoring and evaluating a plan of action within the social science context. Eighty-four undergraduate pre-service teachers were recruited and randomly assigned to one of the four conditions: (1) an IP condition that required students to complete integration prompts, (2) a PP condition that required students to complete procedure prompts, (3) an IPP condition that required students to complete both integration and procedure prompts, or (4) a control condition that did not provide access to any prompts. The findings show that students who received integration prompts outperformed those who did not receive any in knowledge acquisition and problem representation for solving an ill-structured problem. Integration prompts also helped the development and integration of cognitive schema, whereas procedure prompts helped direct students’ attention to specific features of the problem in order to arrive at the solution(s). In fact, the presence of an integration prompt alone is not sufficient to support successful ill-structured problem solving unless a procedure prompt is provided. Based on these findings, this study offers implications for designing Web-based learning environments, engineered to promote integrative knowledge and ill-structured problem solving skills.     

Improving problem solving ability in mathematics by using a mathematical model: A computerized approach

The present study investigates the improvement of students’ mathematical performance by using a mathematical model through a computerized approach. We had developed an intervention program and 11 years students worked independently on a mathematical model in order to improve their self-representation in mathematics, to self-regulate their performance and consequently to improve their problem solving ability. The emphasis of using the specific model was on dividing the problem solving procedure into stages, the concentration on the students’ cognitive processes at each stage and the self-regulation of those cognitive processes in order to overcome cognitive obstacles. The use of the computer offered the opportunity to give students general comments, hints and feedback without the involvement of their teachers. Students had to communicate with a cartoon animation presenting a human being who faced difficulties and cognitive obstacles during problem solving procedure. Three tools were constructed for pre- and post-test (self-representation, mathematical performance and self-regulation). There were administered to 255 students (11 years old), who constituted the experimental and the control group. Results confirmed that providing students with the opportunity to self-reflect on their learning behavior when they encounter obstacles in problem solving is one possible way to enhance students’ self-regulation and consequently their mathematical performance.