Help with a mixed effects model

Hope everyone is safe and sound. I appreciate your help a lot.

I am evaluating two Arabic subtitles of a humorous English scene and asked
263 participants (part) to evaluate the two subtitles (named Standard
Arabic, SA, and Egyptian Arabic, EA) via a questionnaire that asked them to
rank the two subtitles in terms of how much each subtitle is

2) more humorous (hum),

5) closer to Egyptian culture (cul)

The questionnaire contained two 1-10 linear scale questions regarding the 2
points clarified, with 1 meaning the most humorous and closest to Egyptian
culture, and 10 meaning the least humorous and furthest from Egyptian
culture. Also, the questionnaire had a general multiple-choice question
regarding which subtitle is better in general (better). General information
about the participants were also collected concerning gender (categorical
factor), age (numeric factor) and education (categorical factor).

To rule out the effect of which subtitle was watched first, two versions of
the questionnaire were relied on: one showing the ?SA subtitle first? and
another showing the ?EA subtitle first?. Nearly half the participants
answered the first and nearly half answered the latter. The info regarding
this is called WF (watched first).

I am focusing on which social factor/s lead/s the participants to evaluate
one of the two subtitles as generally better and which subtitle is more
humorous and closer to Egyptian culture. Actually, I wanted better to be
the only dependent factor and asking participants 'which subtitle is
better?' could be enough, but I wanted to have detailed information of why
a subtitle is better by asking participants specific questions (regarding
which subtitle is more humorous and closer to Egyptian culture). Most of
the time, the total of the hum + cul = better, but sometimes it is not
(e.g. the sum for subtitle EA could be bigger than for SA, but the
participant prefers SA in the better column).

I thought that mixed effects analyses would clarify the picture and answer
the research questions (which  factor/s lead/s participants to favour a
subtitle over another?) and, so,  tried the lme4 package in R and ran many
models but all the codes I have used are not working.

I ran the following codes, which yielded Error messages, like:

model1<- lmer (better ~ gender + age + education + WF + (1 | part),
data=sub_data)

Error: number of levels of each grouping factor must be < number of
observations (problems: part)

Model2 <- glmer (better ~ gender + age + education + WF + (1 | part), data
= sub_data, family='binomial')

Error in mkRespMod(fr, family = family) :

  response must be numeric or factor

Model3 <- glmer (better ~ age + gender + education + WF + (1 | part), data
= sub_data, family='binomial', control=glmerControl(optimizer=c("bobyqa")))

Error in mkRespMod(fr, family = family) :

  response must be numeric or factor

Why do the models crash? Does the problem lie in the random factor part (which
is a code for participants)? Or is it something related to the mixed
effects analysis?

I hope I am not violating the rules here as I am attaching the dataset (
sub_data) just in case someone would like to have a look at it.

 All the best

--
Saudi Sadiq,