未來研究⽅向

第六章結論與建議

第五節未來研究⽅向

在前⽂，將眼動的分析⽅法分為三⼤類，眼動現象分析、時間與空間分析、

掃視路徑分析，本研究的時間加權分析定位，是位於時間與空間分析的興趣時域分析，但該⽅法也適⽤於掃視路徑分析，例如在掃視路徑的轉換AOI中加⼊時間維度的資訊，建議未來可將掃視路徑分析的時間加權分析進⼀步研究。

本研究主要分析的是凝視眼動現象的分析，本分析⽅法也可適⽤於掃視現象、回視現象等等的眼動現象分析。

應⽤⽅式

本演算法的應⽤⽅式，除了本研究的單詞⾃由回憶測驗外，也可以應⽤在其他需要關注時間要素的眼動歷程情境，例如：學習者學習的⽅式探討，圖⽂參照的⽅式。⾃閉症患者在社會情境中，依據社交事件發⽣時間，進⾏時間加權分析。第⼀印象與眼動現象關聯的研究等等。

案例分析

未來案例分析可做閱讀相關的研究，透過時間加權分析法，研究閱讀的眼動資料之時間分佈與認知的關聯性，⼀⽅⾯可了解閱讀時的認知歷程，另⼀⽅⾯也可提供⽂章結構編排的依據。

本案例分析單詞⾃由回憶測驗是分析單⼀的受試者，其個體的記憶與眼動現象分析，未來研究可試著進⾏群體的眼動分析，但要注意的是，實驗的設計需考量，不同的個體可能會有不同的記憶策略⽅式，⽽不同的記憶策略⽅式，其眼動的模式不相同，因此在群體分析時，可能要在進⾏時間加權分析時，先進⾏分群的分析，將同個記憶策略的群體統計時間加權分析。

與⽣理訊號的資料結合分析

⽣理訊號與時間有⾮常相關的關係，因此使⽤時間加權分析，可以適時的了解眼動資料與⽣理訊號資料的關係，幫助研究者了解視覺刺激材料與⽣理現象的關係，或是了解眼動現象與⽣理現象之間的關聯性。

參考⽂獻

中⽂部分

宋曜廷（2017）。⼤數據語料：分析⼯具、指標建⽴、與教育應⽤-總計畫：⼤數 據語料：分析⼯具、指標建⽴、與教育應⽤。科技部專題研究計畫（編號：

MOST104-2511-S003-017-MY3）。

陳學志、賴惠德、邱發忠（2010）。眼球追蹤技術在學習與教育上的應⽤。教育 科學研究期刊，55（4），39-68。

蔡介⽴（2000）。從眼動控制探討中⽂閱讀的訊息處理歷程：應⽤眼動誘發呈現 技術之系列研究。國⽴政治⼤學⼼理學研究所博⼠論⽂。

西⽂部分

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Hegarty, M., Mayer, R. E., & Monk, C. A. (1995). Comprehension of arithmetic word problems: A comparison of successful and unsuccessful problem solvers. Journal of Educational Psychology, 87(1), 18.

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附錄⼀眼動資料模擬之資料結構

Sample為樣本，Fixation為凝視資料，Aoi為AOI興趣區域，

FixationCalculationResult為「凝視次數加權分析模式」與「凝視次數加權分析模式」的凝視資料計算結果，其Model的資料結構如圖1-1資料結構⽰意圖，型別與定義如表1-1資料結構之型別與定義。

圖 1-1 資料結構⽰意圖

表1-1 資料結構之型別與定義

Models Field 型別定義

Sample _id ObjectId 樣本id

fixations ObjectId Array 凝視資料id陣列

Fixation _id ObjectId 凝視資料id

aoi ObjectId AOI id

time Date 時間

duration Float 持續時間

Aoi _id ObjectId AOI id

code String 編號

FixationCalculationResult _id ObjectId 計算結果id countTimePower Float 次數加權值 durationTimePower Float 持續時間加權值

附錄⼆眼動資料模擬之程式碼

const express = require('express');

const router = express.Router();

const _ = require('lodash');

const gaussian = require('gaussian');

const ss = require('simple-statistics');

router.post('/eye-data', function(req, res, next) { let aoiCodes = req.body['aoiCodes[]'];

let sampleSize = parseInt(req.body['sampleSize']);

let minutes = parseFloat(req.body['minutes']);

let fixationMean = parseFloat(req.body['fixationMean']);

let fixationSd = parseFloat(req.body['fixationSd']);

let saccadeMean = parseFloat(req.body['saccadeMean']);

let saccadeSd = parseFloat(req.body['saccadeSd']);

let opts = {

let report = dataProcess(opts);

res.json({report: report});

});

function dataProcess(opts){

try {

opts.aoiCodes = opts.aoiCodes || ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'];

opts.minutes = opts.minutes || 10;

opts.fixationMean = opts.fixationMean || 295;

opts.fixationSd = opts.fixationSd || 147.5;

opts.saccadeMean = opts.saccadeMean || 35;

opts.saccadeSd = opts.saccadeSd || 17.5;

console.log('opts', opts);

let aoiCodes = opts.aoiCodes;

let minutes = opts.minutes;

let sampleSize = opts.sampleSize;

let fixationMean = opts.fixationMean;

let fixationSd = opts.fixationSd;

let saccadeMean = opts.saccadeMean;

let saccadeSd = opts.saccadeSd;

let seqs = [];

let aoiCodesWithTarget = ['T'].concat(aoiCodes);

let ms = minutes * 60 * 1000;

let codeProb = 1 / ( aoiCodes.length + 1 );

let frontTargetProb = codeProb * .8;

let behindTargetProb = codeProb * 1.2;

let saccadeDistribution = gaussian(saccadeMean, saccadeSd);

let fixationDistribution = gaussian(fixationMean, fixationSd);

let aoiLength = _.round(ms / ( saccadeMean + fixationMean ));

let halfSeqLength = aoiLength / 2;

let generateDataStart;

let generateDataDuration;

let calcDataStart;

let calcDataDuration;

let distribution = gaussian(0, .1);

let pDistribution = gaussian(0, 1);

generateDataStart = _.now();

let code;

saccadeDuration = saccadeDistribution.ppf(Math.random());

time += saccadeDuration + prevDuration;

eye.time = time;

eye.fixationDuration = fixationDistribution.ppf(Math.random());

prevDuration = eye.fixationDuration;

// console.log(eye);

eyes.push(eye);

} });

generateDataDuration = _.now() - generateDataStart;

calcDataStart = _.now();

resultMap[codeHash].timePower = resultMap[codeHash].timePower || 0;

resultMap[codeHash].count = resultMap[codeHash].count || 0;

resultMap[codeHash].fixationDuration = resultMap[codeHash].fixationDuration || 0;

resultMap[codeHash].timePowerIntegral = resultMap[codeHash].timePowerIntegral || 0;

let timePower = calcTimePower(eye.time / timeBase);

let timePowerIntegral = calcTimePowerIntegral(eye.time / timeBase, ( eye.time + eye.fixationDuration ) / timeBase);

eye.timePower = timePower;

eye.timePowerIntegral = timePowerIntegral;

resultMap[codeHash].timePower += timePower;

resultMap[codeHash].fixationDuration += eye.fixationDuration;

resultMap[codeHash].count++;

resultMap[codeHash].timePowerIntegral += timePowerIntegral;

let avgTimePowers = [];

_.keys(resultMap).forEach((key) => { let r = resultMap[key];

r.avgTimePower = r.timePower / r.count;

r.avgFixationDuration = r.fixationDuration / r.count;

r.avgTimePowerIntegral = r.timePowerIntegral / r.count;

timePowers.push(r.timePower);

avgTimePowers.push(r.avgTimePower);

avgFixationDurations.push(r.avgFixationDuration);

timePowerIntegrals.push(r.timePowerIntegral);

avgTimePowerIntegrals.push(r.avgTimePowerIntegral);

counts.push(r.count);

fixationDurations.push(r.fixationDuration);

});

let timePowerMean = ss.mean(timePowers);

let timePowerSd = ss.standardDeviation(timePowers);

let avgTimePowerMean = ss.mean(avgTimePowers);

let avgTimePowerSd = ss.standardDeviation(avgTimePowers);

let avgTimePowerIntegralMean = ss.mean(avgTimePowerIntegrals);

let avgTimePowerIntegralSd = ss.standardDeviation(avgTimePowerIntegrals);

let timePowerIntegralMean = ss.mean(timePowerIntegrals);

let timePowerIntegralSd = ss.standardDeviation(timePowerIntegrals);

let countMean = ss.mean(counts);

let countSd = ss.standardDeviation(counts);

let fixationDurationMean = ss.mean(fixationDurations);

let fixationDurationSd = ss.standardDeviation(fixationDurations);

let avgFixationDurationMean = ss.mean(avgFixationDurations);

let avgFixationDurationSd = ss.standardDeviation(avgFixationDurations);

_.keys(resultMap).forEach((key) => { let r = resultMap[key];

r.timePowerZ = ss.zScore(r.timePower, timePowerMean, timePowerSd);

r.timePowerAlpha = ( 1 - pDistribution.cdf(Math.abs(r.timePowerZ)) ) * 2;

r.avgTimePowerZ = ss.zScore(r.avgTimePower, avgTimePowerMean, avgTimePowerSd);

r.avgTimePowerAlpha = ( 1 - pDistribution.cdf(Math.abs(r.avgTimePowerZ)) )

* 2;

r.timePowerIntegralZ = ss.zScore(r.timePowerIntegral, timePowerIntegralMean, timePowerIntegralSd);

r.timePowerIntegralAlpha = ( 1 -

pDistribution.cdf(Math.abs(r.timePowerIntegralZ)) ) * 2;

r.avgTimePowerIntegralZ = ss.zScore(r.avgTimePowerIntegral, avgTimePowerIntegralMean, avgTimePowerIntegralSd);

r.avgTimePowerIntegralAlpha = ( 1 -

pDistribution.cdf(Math.abs(r.avgTimePowerIntegralZ)) ) * 2;

r.countZ = ss.zScore(r.count, countMean, countSd);

r.countAlpha = ( 1 - pDistribution.cdf(Math.abs(r.countZ)) ) * 2;

r.fixationDurationZ = ss.zScore(r.fixationDuration, fixationDurationMean, fixationDurationSd);

r.fixationDurationAlpha = ( 1 -

pDistribution.cdf(Math.abs(r.fixationDurationZ)) ) * 2;

r.avgFixationDurationZ = ss.zScore(r.avgFixationDuration, avgFixationDurationMean, avgFixationDurationSd);

r.avgFixationDurationAlpha = ( 1 -

pDistribution.cdf(Math.abs(r.avgFixationDurationZ)) ) * 2;

});

let results = [];

r.aoiCode = aoiCode;

results.push(r);

} });

calcDataDuration = _.now() - calcDataStart;

let report = {};

report.resultMap = resultMap;

report.results = results;

report.eyes = eyes;

report.opts = opts;

report.seqs = seqs;

report.aoiCodes = aoiCodes;

report.aoiCodesWithTarget = aoiCodesWithTarget;

report.timePowerMean = timePowerMean;

report.timePowerSd = timePowerSd;

report.timePowerCv = timePowerSd / timePowerMean;

report.avgTimePowerMean = avgTimePowerMean;

report.avgTimePowerSd = avgTimePowerSd;

report.avgTimePowerCv = avgTimePowerSd / avgTimePowerMean;

report.avgTimePowerIntegralMean = avgTimePowerIntegralMean;

report.avgTimePowerIntegralSd = avgTimePowerIntegralSd;

report.avgTimePowerIntegralCv = avgTimePowerIntegralSd / avgTimePowerIntegralMean;

第六章 結論與建議

第五節 未來研究⽅向

參考⽂獻

附錄⼀ 眼動資料模擬之資料結構

附錄⼆ 眼動資料模擬之程式碼

第六章結論與建議

第五節未來研究⽅向

附錄⼀眼動資料模擬之資料結構

附錄⼆眼動資料模擬之程式碼