Handbook of Individual Differences, Learning, and Instruction

Jonassen, David H.; Grabowski, Barbara L.

doi:10.4324/9780203052860

Cited by 372 publications

(310 citation statements)

References 0 publications

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“…In this study, the analysis of learning features revealed not only that the learning features are very different from each other but that most of the learning features of visualisers and verbalisers are also similar to those described in previous studies (eg, Childers et al , 1985; Jonassen & Grabowski, 1993; Riding & Sadler‐Smith, 1997). The visualisers and verbalisers in this study were similar in their interpretation of the operation results of DLMRs but very different in the other three dimensions, including their ways of reading learning guides, ways of operating DLMRs and strategies for using DLMRs.…”

Section: Discussionsupporting

confidence: 72%

“…On the other hand, verbalisers tended to read the learning guide carefully and completely, to operate DLMRs in a task‐oriented way and to use visualisation strategies when operating DLMRs. Some of these features confirmed the differences in the literature between visualisers and verbalisers, such as in the way they read text (Riding & Sadler‐Smith, 1997) and the way they orient themselves to their work (Childers et al , 1985; Jonassen & Grabowski, 1993).…”

Section: Discussionsupporting

confidence: 57%

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Can verbalisers learn as well as visualisers in simulation‐based CAL with predominantly visual representations? Preliminary evidence from a pilot study

Liu¹,

Kinshuk²,

Lin³

et al. 2011

Brit J Educational Tech

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Simulation-based computer-assisted learning (CAL) is emerging as new technologies are finding a place in mainstream education. Dynamically linked multiple representations (DLMRs) is at the core of simulation-based CAL. DLMRs includes multiple visual representations, and it enables students to manipulate one representation and to immediately receive feedback from others. An interesting and important research question is whether verbalisers, who prefer to process verbal material, have similar learning performance and learning features as visualisers, who prefer to process visual material. To answer this question, 28 undergraduate students were selected as participants from the 855 undergraduate students who were initially tested with the style of processing scale (SOP). They were representative of either visualisers or verbalisers (students who scored upper 10% and lower 10% on the SOP). A study was conducted using an experimental design that included pre-and posttest and thinking-aloud methods. Simulation-Assisted Learning Statistics (SALS) was adopted as the learning environment for both groups. The analysis results are based on the data of 25 participants because three participants had trouble thinking aloud while using SALS. The results indicated that the visualisers and verbalisers did not differ significantly in their learning performance, but they did exhibit significantly different learning features in their use of DLMRs, their methods of reading learning guides and their learning strategies. Additionally, the learning features of the verbalisers explained why their learning performance was similar to that of the visualisers. Finally, this study provides recommendations for future applications and studies of simulation-based CAL.

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Section: Discussionsupporting

confidence: 72%

Section: Discussionsupporting

confidence: 57%

Can verbalisers learn as well as visualisers in simulation‐based CAL with predominantly visual representations? Preliminary evidence from a pilot study

Liu¹,

Kinshuk²,

Lin³

et al. 2011

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…As pointed out by Jones (), young people in each generation are “a mixed group of people with different interests, motives, [abilities], and never a single generational cohort with common characterises” (p 30). Some educators (eg, Jonassen & Grabowski, ) also dispute the claim that a generation of young learners has a particular learning style or learning preference, as learning preferences should not be viewed as fixed and universally generalisable. Furthermore, according to Biggs (), students may also change their learning approaches based on their perceptions of the task requirements and past experiences.…”

Section: Introductionmentioning

confidence: 99%

Technology use and learning characteristics of students in higher education: Do generational differences exist?

Lai¹,

Hong²

2014

Brit J Educational Tech

189

118

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As digital technologies form an inextricable part of young people's everyday lives, some commentators claim that the current generation of learners think and learn differently from their predecessors. This study investigated the validity of this claim by surveying 799 undergraduate and 81 postgraduate students at a large research-intensive university in New Zealand to document their use of digital technologies on university and social activities and comparing three age groups of students (under 20, 20-30 and over 30) to see whether there were any differences in their learning characteristics. The findings of the study showed that while students spent a large amount of time on digital technologies, the range of digital technologies they used was rather limited. There were also no practical generational differences in the technology use pattern and learning characteristics found in this study. The results of this study suggest that generation is not a determining factor in students' use of digital technologies for learning nor has generation had a radical impact on learning characteristics of higher education students.

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“…Each learner has individual needs and characteristics such as different prior knowledge, cognitive abilities, learning styles, motivation and so on. These individual differences affect the learning process and are the reason why some learners find it easy to learn in a particular course, whereas others find the same course difficult (Jonassen & Grabowski 1993).…”

Section: Introductionmentioning

confidence: 99%

Analysis of learners' navigational behaviour and their learning styles in an online course

Graf

Liu

Kinshuk

2010

Computer Assisted Learning

156

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Providing adaptive features and personalized support by considering students' learning styles in computer-assisted learning systems has high potential in making learning easier for students in terms of reducing their efforts or increasing their performance. In this study, the navigational behaviour of students in an online course within a learning management system was investigated, looking at how students with different learning styles prefer to use and learn in such a course. As a result, several differences in the students' navigation patterns were identified. These findings have several implications for improving adaptivity. First, they showed that students with different learning styles use different strategies to learn and navigate through the course, which can be seen as another argument for providing adaptivity. Second, the findings provided information for extending the adaptive functionality in typical learning management systems. Third, the information about differences in navigational behaviour can contribute towards automatic detection of learning styles, helping in making student modeling approaches more accurate.

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Handbook of Individual Differences, Learning, and Instruction

Cited by 372 publications

References 0 publications

Can verbalisers learn as well as visualisers in simulation‐based CAL with predominantly visual representations? Preliminary evidence from a pilot study

Can verbalisers learn as well as visualisers in simulation‐based CAL with predominantly visual representations? Preliminary evidence from a pilot study

Technology use and learning characteristics of students in higher education: Do generational differences exist?

Analysis of learners' navigational behaviour and their learning styles in an online course

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