2016年2月16日 星期二

Re: 北醫大學生申訴評議委員會

1. 我有收到,但信封之粗糙,缺乏官籤,讓我覺得像是有人冒用學校和圖書館的名義寄送。所以我當時不認為這是學校正式的回覆。而看過內容後,更是讓我大失所望。
2. 我能透漏的只有已經向科技部檢舉,其他的還有相當多的管道。但我不會全盤告訴你。因為我覺得北醫想粉飾太平。這讓我非常失望。資訊不對等的情況下,我只能保護自己。畢竟已經遭受這樣的迫害,而第一次、第二次拿到的回應竟然也是這樣。所以我對透漏我檢舉的單位,還有保留的必要性。
3. 這點我知道,而我最近還沒時間去提告,但已經在擬狀。

不客氣,希望北醫認真看待此事。
就我收到的訊息,我認為北醫並不明白此事影響層級應該是相當高的。否則也不會到了這個時候才致電問我。在這種情況下,應該用非常認真積極的態度和更高層級的人士出面來處理這件事情。畢竟沒有必要因為一個職員和被開除的工讀生,而拖累整個學校在教育部和科技部面前的形象。事證其實相當明顯,我想所有的問題點也都提過了。希望明天的審查會各位還能仔細思慮。






秘書處 蘇韻憲(TMU) <asdf@tmu.edu.tw> 於 2016年2月16日 下午8:16 寫道:
親愛的吳同學您好:

我是北醫大學生申訴評議委員會承辦人敝姓蘇
不好意思這麼晚打擾您~
由於您在2/2提出申訴,而本委員會將於近日就本案召開學生申訴評議委員會
在此之前,為了提供委員完整的資料,是否能懇請教您相關問題:
1.請問您是否有收到圖書館的書面回覆(2/5)email(2/7)
2.如方便懇請告知委員您目前有向那些單位申訴,以及大致申訴的內容,是否有相關資料
3.您在申訴書提到就本申訴事件將提告,而就本校學生申訴暨處理辦法第十三條規定
申訴人就申訴案件或相牽連之事件,同時或先後另行提起訴願、行政訴訟、民事訴訟或刑事訴訟者,應即以書面通知本會。
再請您特別留意

另剛電話中有提到是否需出席,先以提供本辦法第十四條規定如下,請您酌參
本會會議之召開以不公開為原則,必要時得經委員會議決議邀請申訴人、關係人、學者專家或本校有關指派之人員到場說明。

最後關於您所提供予本會之資料,本委員會均應予保密
以上
懇請不吝說明
萬分感激

臺北醫學大學 蘇韻憲敬上
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蘇韻憲 Yunxian Su
臺北醫學大學|秘書處|校務企劃組
  話:02-2736-1661轉分機2085
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--
吳浩民 Howard Wu
政治大學英語教學所  102551020@nccu.edu.tw
您可以透過 https://db.tt/XtqMCbI  加入dropbox

我是Ptt以下三個板的板主,歡迎跟我討論這些話題
Battery, Browsers, Scenarist
或是有關Linux、自由軟體、電動車、英語教學、語言學、哲學、西洋文學

北醫的官樣回復

這就是完全不承認自己有錯吧?
讓我發現他們完全沒有意思要改進。

恩…這意思其實就是要我告到底,不要讓他有機會疏通其他管道和高層。
很抱歉,我也只是做該做的事情而已。

Research proposal v6

Do Mnemonic-devices placed on main verbs and predicates enchance people's average reading fluency?






Howard Wu

102551020@nccu.edu.tw

Advisor: Prof. J.L Tsai

Due date 2016. 1/18 5pm













View this document with latest status online

Abstract

In this research proposal I try to figure out if m-devices placed on ceartain sytactic position like main verbs and predicates will enhance reading fluency. By placing them in the correct positions, I assume they will improve readers' reading speed and decrease unnecessary regression and fixations.

I will calculate the mean (average) velovity and mean acceleration on two neighboring fixations. The data obtained by eye-tracking devices will provide accurate horizontal positional data for my research, thus answering my desired questions as the paper titled. And this will probably lead to the invention of an hint for POS(part of speech) based m-devices reading training for dyslexics who suffer from crowding effect and poor working memory.  


introduction & literature review

From the abstract of "Shorter lines facilitate reading in those who struggle." , it was written "...Given that an expected trade-off between horizontal and vertical regression was not observed when line lengths were altered, we speculate that these effects occur because sluggish attention spreads perception to the left as the gaze shifts during reading. Short lines eliminate crowded text to the left, reducing regression. " I think it might also be influential if we can utilize their limited attention to the predicates by mnemonic-devices. No such experiment has been done by eye tracking devices.

This time I need to produce the text by myself, and adjust the spacing of the mnemoinc devices so that they won't look crowded. I suspect the result that was opposite to my prediction last time was caused by crowding effect. Those m-devices were added by marking software (Skitch, made by evernote. corp) after the text sample was produced. So they look somehow crowded and awakard.



Methods: (design, material, procedure…)

From my previous experiment I found some drawbacks and I would like to avoid some of them by improving the techniques of experiment design. The rest setting and my hypothesis will remain the same. The material will be specially written with specific details. For example, a description of the objects and their orientation in the room.


I would not choose text from source such as newspaper on NYT or science textbook , even they might have more narrative materail with neutral facts. Some researchers was  claiming they randomly selcet articles from the science textbook, but it is still possible that some participants have more understanding on such a field, thus causing them to read faster and better. Because they may still have bias and hints for readers of some backgrounds. This would influence their processing speed of the text. So I'd write a novel article on my own.

For questions like how people process articles with different styles and syntatic attributes, I think it shall be postponed until I make clear of this m-devices question. But I also want to know on which material my m-devices work better. They will be arranged in the experiment next time.

  • α = velocity for each trial

  • β = acceleration for each trial

  • R = observed ROIs(Region of interest), where positive acceleration happens and ROI marks were recorded

Here are the distribution maps of the above data:

Let me list out the problems encountered so far.

  1. the Y-axis alignment is corrupted so there are dots outside the ROI. But they shall be counted into the ROI.

  2. Grouping of the test is problematic: the best grouping shall be a 2x2 matrix, or at leasat a 2x2 matrix. But here in this case, mine is a 2x1 matrix.

  3. Ways of defining efficiency. I use the looseness of the dot as an indication for reading speed. Yes we do have time stamp of each dot. But these "looseness" can not faithfully explain if their reading is efficient.

  4. the way they scan through the passage. (they just move their eyes without really reading it!)

Some methods to improve such problems

  1. fill up the whole map with ROI, so that no dots will be missed.

  2. I will make my next experiment grouping at least in 2x2 matrix.

  3. I try to make an operational definition on this: higher mean velocity throughout the trial. By mean velocity I mean the average score of each neighboring fixation. Of course I need to delete the huge regression caused by line changing.

  4. We will tell them there's a quiz after this eye-tracking experiment. I am not if I shall make one quiz paper at all. But it would be a good reference to tell their VAS (visual attentional span) in another way. Just a longer term VAS after the test.

  5. I may need a VAS for them before my test so that I could know if the participants have relatively similar working memory. I will also make sure the participants did not take pills or feel tired because they have to hurry to the test or ride fast. All these factors will cause their attention lose and dry eyes, which is very bad for reading and eye tracking calibration.  

ROI details in x-y map along with fixation map

http://fall-cicada.blogspot.tw/2015/12/dot-map-comparison.html


Mode of alpha and beta for 701 and 702 in trial 2 and trial 3

We also want to provide the δ^2(=variance) and mean of alpha/beta of all trials of both participants (alpha 2 belongs to trial 2; beta 3 belongs to trial3, etc ). Please see endnote. Though I have no idea on how to use them correctly, yet.


Now we only focus on the trial 2 & 3 of participant 701 & 702.

For Trial 2 of Participant 701: There are 37 sections (defining as neighboring fixations) with positive slope. That is, positive acceleration. There are 15 of them located within ROIs, where we can observe for the m-devices effect. I provide the observed ration to elaborate how much fidelity my experiment carries. In this case, prediction made based on 30%~40% obsered ratio is not very representative.

To address easier, I made a table below. Due to my limited time on data processing, I can only calculate so much. But I will keep on to find out if the rest answers are still so depressing.

(β > 0)

Δ x

R = observed Δ x

observed ratio

mean α throughout trial

mean β throughout trial

mean α of R

mean β of R

701 trial2

37

15

40.5%

0.329601


0.301910


702 trial2

37

12

32.4%





701 trial3

43

13

30.2%

0.312848


-0.2827


702 trial3

55

17

30.9%









Anticipating results

I expect the readers will experience better reading fluency after they get to known the tricks of predicates and main verbs. The foreground is that this would not cause extra crowding effect for them. The m-devices could be annoying for readers since they were placed and add up afterward. This would make the whole text looks crowded. I need to increase the line space greatly or clear up the layout to make the words look right.

I was expecting the group with m-devices will read far faster and has fewer dots upon the ROI of predicates.

But I figure out:
mean alpha throughout the trial is actually larger than the mean alpha of R !

This means my m-devices are only slowing them down!


To my surprise, the result seems to be the contrary to my prediction. I had deleted the huge regressions of line changing. So they shall not cause the trouble. I am not sure what happened to my data, I am not even sure if I process the data analysis correctly. (It should be correct)

Maybe I should consider my methods on calculating the "averaged" (mean) velocity is not suitable. I can not just delete the outliers caused by line changing then average them. Even though these spots(fixations) can be clearly markerd on the data. I still hold some faith to my hypothesis, but I need to renew staticstics knowledge.



Reference

  1. Olson, R. K., Kliegl, R., & Davidson, B. J. (1983). Dyslexic and normal readers' eye movements. Journal of Experimental Psychology. Human Perception and Performance, 9(5), 816–825. http://doi.org/10.1037/0096-1523.9.5.816

  2. Schneps, M. H., Thomson, J. M., Chen, C., Sonnert, G., & Pomplun, M. (2013). E-Readers Are More Effective than Paper for Some with Dyslexia. PLoS ONE, 8(9), e75634. http://doi.org/10.1371/journal.pone.0075634

  3. Stanley, G., Smith, G. a, & Howell, E. a. (1983). Eye-movements and sequential tracking in dyslexic and control children. British Journal of Psychology (London, England : 1953). http://doi.org/10.1111/j.2044-8295.1983.tb01852.x

  4. http://www.degruyter.com/view/j/cogl.2015.26.issue-2/cog-2014-0066/cog-2014-0066.xml?format=INT

Endnote:

Complete charts for both participants.


mode

701

702

α1

0~0.5

0~0.5

α2

0~0.5

0~0.65

α3

0~0.5

0~0.4

α4

0~0.5

0~0.65

α5

0~0.65

0~0.5

α6

0~0.5

0~0.7

α7

0~0.5

0~0.5

α8

0~0.5

no data

α9

0~0.5

0~0.5



mode

701

702

β1

-0.0035~0

-0.0035~0

β2

-0.004~0

-0.005~0

β3

-0.015~0

-0.004~0

β4

-0.004~0

0~0.015

β5

-0.0065~0

0~0.005

β6

-0.005~0

-0.005~0.005

β7

-0.005~0

0~0.0075

β8

0~0.005

no data

β9

-0.01~0.01

0~0.005



701

μ of α

μ of β

δ^2 of α

δ^2 of β

1

0.004146444621

-0.0000244505898

0.815874143

0.00004458453974

2

0.002332514632

-0.000008664773698

0.8333474164


3





4





5





6





7





8





9







702

μ of α

μ of β

δ^2 of α

δ^2 of β

1





2





3





4





5





6





7





8





9








大紅換後輪

更換里程  39478 km
前面的煞車皮也一起換了,用了一萬多公里出頭,馬吉斯的輪胎還是不錯的。
我說不錯是因為到了這個該更換的警戒線,其實防滑能力還不錯。只有滑過一次。



--
吳浩民 Howard Wu
政治大學英語教學所  102551020@nccu.edu.tw
您可以透過 https://db.tt/XtqMCbI  加入dropbox

我是Ptt以下三個板的板主,歡迎跟我討論這些話題
Battery, Browsers, Scenarist
或是有關Linux、自由軟體、電動車、英語教學、語言學、哲學、西洋文學

改良版貓避寒所

我作成雙翼造型,家中的貓馬上就有興趣躲進去了。
我才發現果然貓喜歡的躲藏箱,是有特定造型的。現在裡面放了一團衣服,我會再找好地方放這個筒子。





特殊研究的合作投產詢問


您好:

我是政治大學的三年級研究生,我設計了一款轉盤,我想華碩可能有興趣參與試產或專利申請。
我因為沒有其他管道可以聯繫華碩或和碩高層,所以只得找這個「高階人才聯絡管道」毛遂自薦
畢竟需要公司的高層願意投資或了解這個計畫對你們的收益會是多大。
就我自己的建議,可以作為實體的教具和內建在每一隻華碩手機裡面的app。
對有硬體經驗的華碩來說,是一個很特殊的機緣。對於出口到英語系或想學英語的國家,很有吸引力。

我做研究的單位(英語教學所 /  追蹤眼動閱讀實驗室 EMR lab),沒有設備和部門能生產這樣的東西。我影片中的原型其實是我自己用紙板做出來的。我請了新北高工的劉正評老師幫我做了幾個版本的cad檔案,可以 展示我想要的轉盤會是什麼樣子。
這款轉盤有一些特色會需要借重華碩的硬體工藝:他的材質和觸感、配重和內建振動回饋的幾種差異、以及最特別的,內建微電腦和句法檢測的程式。
他就像是一個圓盤的遊戲手把,會振動,也有晶片判斷句法的正確性來提示學習者的造句。
目前這個計畫我只有向華碩,以及和碩提起。我雖然住在土城,但鴻海不是我優先找合作的對象,因為和他的合作包括連專利申請和後續研發我都有疑慮。
即使到土城工業區只有不到十分鐘的車程,我寧願通勤一小時多到北投去做研究,就是認同華碩在材質處理上的能力和ZenUI的設計哲學。

而國內我目前只打算向幾個非業界的投資者提起,國外的部份,受限於我沒有英美的帳戶。所以資金的流入會有困難。
但我打算向三位名人聯繫告知這個計畫,再來等待他們的接洽應變。

他們是 Dean Kamen (Segway, slingshot的發明人,DEKA的老闆)、Richard Branson(Virgin airline和spaceship one老闆,),以及Bill Gates(微軟和xbox的老闆,我對他們的遊戲機控制手把設計特別讚賞,轉盤的振動回饋希望能有這樣的能力)
前面兩位都有閱讀障礙,但卻能創業和製作許多偉大發明。這三位都對教育改革和科技發明能改善世界有相當信心和興趣。
會找上他們,正是因為我認為在個性氣味上,理念上,我相信他們會有興趣討論我的發明。而國內的企業家,我最敬佩的就是施崇堂先生和童子賢先生了。

我錄製了一段中文影片。
Multi-layered Disk for learning abstract English syntax 




你們公司有一些工讀生職缺,不知道能否給我一個機會去實現這個計畫,並以科學的方式去驗證,商業的方式去推行。
並且學習新事物呢?謝謝!


由於我剛剛失業,進入研究所以來我一直都是自己打理生活費,現在一下子就陷入困頓。
也希望公司如果有機會,能給我一個位置。
再次謝謝!



--
吳浩民 Howard Wu
政治大學英語教學所  102551020@nccu.edu.tw
您可以透過 https://db.tt/XtqMCbI  加入dropbox

我是Ptt以下三個板的板主,歡迎跟我討論這些話題
Battery, Browsers, Scenarist
或是有關Linux、自由軟體、電動車、英語教學、語言學、哲學、西洋文學