# A Literature Review Comparing Experts’ and Non-Experts’ Visual Processing of Graphs during Problem-Solving and Learning

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Literature Review

- Comparison of experts vs. non-experts (population)
- STEM subject (domain)
- Learning or problem-solving with graphs, diagrams, or functions (intervention)
- Analysis of visual behavior via eye-tracking metrics (outcome)
- Empirical study
- Full text available in English

#### 2.2. Data Extraction

- Year of publication
- STEM subject in which the study was conducted
- Type of graph
- Eye-tracking metrics
- Areas of interest (AOIs) used for the analysis of eye-tracking metrics
- Expertise determination
- Key findings

## 3. Results

#### 3.1. Publication Period

#### 3.2. Domains and Types of Graphs

#### 3.3. Determination of Expertise

#### 3.4. Eye-Tracking Metrics

#### 3.5. Gaze Behavior of Experts and Non-Experts

#### 3.5.1. Macro- and Meso-Level

#### 3.5.2. Micro-Level

## 4. Discussion

#### 4.1. Summary of Experts’ and Non-Experts’ Visual Strategies

#### 4.1.1. Overview of Eye-Tracking Metrics

#### 4.1.2. Meso-and Macro- vs. Micro-Level AOIs

#### 4.1.3. Visual Strategies of Experts and Non-Experts during Problem-Solving and Learning with Graphs

#### 4.2. Limitations

#### 4.3. Future Research

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Overview of the number of studies related to the visual behavior of experts and non-experts’ during learning and problem-solving with graphs per year (

**top left**); number of studies using graphs of a certain subject (multiple mentions are possible,

**top right**); types of graphs used in the studies (

**middle left**); overview of the measure for determining expertise (multiple mentions are possible,

**middle right**); overview of eye-tracking metrics used in the studies included in the literature review (

**low left**); number of eye-tracking metrics used for analyzing visual behavior when looking at graphs (

**low right**).

**Figure 2.**The number and types of eye-tracking metrics used in studies investigating the visual behavior of experts and non-experts learning or problem-solving with physics graphs.

Categories | Terms |
---|---|

Visual behavior | “eye tracking”, “viewing behavior”, “visual attention” |

Graphs | “graph”, “diagram”, “function” |

**Table 2.**Overview over studies included in the literature review, including eye-tracking metrics (FD: fixation duration, FC: fixation count; DT: dwell time; S: saccades; FG: first gaze; PS: pupil size; T: transitions; NRV: number of revisits; AOI: area of interest; SD: standard deviation).

Reference | Year of Publication | Subject | Graph Type | Determination of Expertise | Eye-Tracking Metrics |
---|---|---|---|---|---|

Ahmed et al. | 2021 | Engineering | Line graphs | Professionals | FD (average, total), FC (average, total) |

Atkins and McNeal | 2018 | Geoscience | Line and bar graphs | Pre-test | FD (normalized, total) |

Brückner et al. | 2020 | Physics, Economics | Line graphs | Domain | DT (total, on relevant AOIs) |

Dzsotjan et al. | 2021 | Physics | Line graphs | Learning gain | Multiple features including DT (total, mean; SD of both) |

Harsh et al. | 2019 | Biology | Line graphs, diagrams | Level of study | FC (normalized), DT (normalized), S (normalized) |

Huang and Chen | 2016 | Physics | Diagram | Spatial working memory | DT (average), FC (total stimulus, on AOIs), FG, PS, S |

Ho et al. | 2014 | Biology | Line graphs | Prior knowledge | FD (total), T, NRV |

Kekule | 2014 | Physics | Line graphs | Performance | Heat maps based on FC |

Keller and Junghans | 2017 | Medicine | Line graphs | Numeracy | FD (relative), FC (relative) |

Kim et al. | 2014 | Math | Line graphs | Dyslexia | DT, FG. |

Kim and Wisehart | 2017 | Math | Bar graphs | Dyslexia | DT, T |

Klein et al. | 2019 | Physics, Finance | Line graphs | Domain | DT (total; AOI and entire stimulus), FC (average; AOI), S |

Klein et al. | 2020 | Physics | Line graphs | Performance | DT |

Kozhevnikov et al. | 2007 | Physics | Line graphs | Spatial ability | FD (relative) |

Küchemann et al. | 2020 | Physics | Line graphs | Performance | DT |

Küchemann et al. | 2021 | Physics | Line graphs | Performance | DT (total, relative), T |

Madsen et al. | 2012 | Physics | Diagrams, line graphs | Performance | FD (normalized; overall, first two seconds) |

Okan et al. | 2016a | Medicine | Line and bar graphs | Graph literacy | FD (total) |

Okan et al. | 2016b | Medicine | Line and bar graphs | Graph literacy | FD |

Peebles and Cheng | 2003 | Economics | Line graphs | NA ^{†} | Not applicable |

Richter et al. | 2021 | Economics | Line graphs | Prior knowledge | DT, FG, T, PS |

Rouinfar et al. | 2014 | Physics | Diagram | Performance | Domain relative ration (relative dwell time /relative area of AOI) |

Skrabankova et al. | 2020 | Physics | Line graphs | Teacher’s opinion | T, FC |

Strobel et al. | 2019 | Various topics | Bar graphs | Working memory capacity | FD (total) |

Susac et al. | 2018 | Physics, Finance | Line graphs | Domain | DT |

Tai et al. | 2006 | Various topics | Line graphs | Domain | FD, DT, S |

Toker et al. | 2013 | Evaluating student performance | Bar and radar graphs | Working memory capacity, visualization experience | FD (total, relative, mean, SD), FC (total, relative), S,T |

Toker and Conati | 2014 | Data analysis | Bar graphs | Perceptual speed, working memory | FC, FD, S |

Viiri et al. | 2017 | Physics | Line graphs | Performance | Heat maps |

Vila and Gomez | 2016 | Economics | Bar graphs | Performance | DT |

Yen et al. | 2012 | Physics, various topics | Line graphs | Domain | DT (normalized), FC |

Zhu and Feng | 2015 | Math | Line graphs | Performance | T |

Viiri et al. | 2017 | Physics | Line graphs | Performance | Heat maps |

Vila and Gomez | 2016 | Economics | Bar graphs | Performance | DT |

Yen et al. | 2012 | Physics, various topics | Line graphs | Domain | DT (normalized), FC |

Zhu and Feng | 2015 | Math | Line graphs | Performance | T |

^{†}Comparison with a scanpath assumed optimal for the task.

**Table 3.**Overview of findings of studies analyzing eye-tracking metrics based on meso- and macro-level AOIs.

Dependent Variable | Findings and References |
---|---|

Fixation duration | Experts have longer average fixation durations, but spend a shorter time on the graph than non-experts [50] Experts have the same fixation duration on a graph as non-experts [55,58] Experts fixate less on seductive details [54] Experts pay more attention to trends than non-experts, but non-experts pay more attention to the title and the axes [23] Experts look longer at the graph than non-experts ([42]; [10], experiment 2, only for conflicting graphs) Experts look longer at relevant areas (experiment 1 [10]; [59]) Experts look less at irrelevant axes’ labels [54,55] |

Fixation count | On average, experts fixate less often on graphs than non-experts [43,58] Experts and non-experts make the same number of fixations [49] Experts look less often at irrelevant regions [55] |

Transitions | Experts transitioned less often between a graph and text [39,51] Experts switch more often between graphs and between graphics and text than non-experts [42] Experts made “more strategic transitions among AOI triples” [40] (p. 1) Experts made fewer transitions than non-experts on harder tasks [48] Experts made the same relative number of transitions as non-experts (experiment 1 [10]) |

First gaze/fixation | Experts initially spend more time on the graph than non-experts [58] Experts look at the graph data later than non-experts [60] |

Dwell time | Non-experts spend more time on the graph than experts [36,38] There are no differences in total dwell time between experts and non-experts [11] Experts look longer at the correct answer [45] Experts (i.e., students without dyslexia) paid less attention to the x-axis [39] |

Saccades | Experts make fewer saccades than non-experts [43] |

Revisits | Experts visit the graph more often than non-experts [42] |

Dependent Variable | Findings and References |
---|---|

Fixation duration | Experts spend more time on graph information (such as title and variables) than non-experts [41,46] Experts look at the entire graph [1] Experts spend more time on relevant areas [1,37,47] |

Fixation count | Experts fixate on the axes more often [35] Experts visit graph information (such as title and variables) more often than non-experts [41] Experts fixate more often on task-relevant AOIs [37] |

Transitions | Experts transition more often between conceptually relevant areas [53] |

Revisits | Experts study the axes, axes labels and line segments more often [35] |

Dwell time | Experts look longer at conceptually relevant areas [52,53,56] Experts spend less time on areas that can be used to calculate the solution [53] Experts spend less time on areas found relevant for non-experts [56] |

Saccades | Experts look along the graph slope [1] |

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**MDPI and ACS Style**

Ruf, V.; Horrer, A.; Berndt, M.; Hofer, S.I.; Fischer, F.; Fischer, M.R.; Zottmann, J.M.; Kuhn, J.; Küchemann, S.
A Literature Review Comparing Experts’ and Non-Experts’ Visual Processing of Graphs during Problem-Solving and Learning. *Educ. Sci.* **2023**, *13*, 216.
https://doi.org/10.3390/educsci13020216

**AMA Style**

Ruf V, Horrer A, Berndt M, Hofer SI, Fischer F, Fischer MR, Zottmann JM, Kuhn J, Küchemann S.
A Literature Review Comparing Experts’ and Non-Experts’ Visual Processing of Graphs during Problem-Solving and Learning. *Education Sciences*. 2023; 13(2):216.
https://doi.org/10.3390/educsci13020216

**Chicago/Turabian Style**

Ruf, Verena, Anna Horrer, Markus Berndt, Sarah Isabelle Hofer, Frank Fischer, Martin R. Fischer, Jan M. Zottmann, Jochen Kuhn, and Stefan Küchemann.
2023. "A Literature Review Comparing Experts’ and Non-Experts’ Visual Processing of Graphs during Problem-Solving and Learning" *Education Sciences* 13, no. 2: 216.
https://doi.org/10.3390/educsci13020216