Efektivitas Strategi Ta’bir Mushawwar dalam Pembelajaran Bahasa Arab di Madrasah Ibtidaiyah
Keywords:
Arabic; speaking skill; ta’bir mushawwar
Abstract
Speaking proficiency is one of the main skills in Arabic language learning, but fourth grade students of MI TPI Keramat face difficulties in assembling mufradat and practicing active conversation, mainly due to the lack of varied learning strategies. This study aims to analyze the effectiveness of the ta'bir mushawwar strategy, which uses picture as a media to facilitate students in constructing sentences and telling stories, in improving Arabic speaking skills. With a quantitative approach and pre-experiment design, this study involved 18 students of class IV-C. Data were collected through tests, observations, and interviews, then analyzed descriptively and N-Gain test. The posttest average was 83.06 (very good category) with 88.9% completeness, and the N-Gain score was 0.6398 which showed effectiveness in the medium category. The ta'bir mushawwar strategy offers a solution in the form of a visual and hands-on learning approach that can significantly improve students' speaking skills and make learning more interesting and interactive.
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"""
csv.py - read/write/investigate CSV files
"""
import re
from _csv import Error, __version__, writer, reader, register_dialect, \
unregister_dialect, get_dialect, list_dialects, \
field_size_limit, \
QUOTE_MINIMAL, QUOTE_ALL, QUOTE_NONNUMERIC, QUOTE_NONE, \
__doc__
from _csv import Dialect as _Dialect
from io import StringIO
__all__ = ["QUOTE_MINIMAL", "QUOTE_ALL", "QUOTE_NONNUMERIC", "QUOTE_NONE",
"Error", "Dialect", "__doc__", "excel", "excel_tab",
"field_size_limit", "reader", "writer",
"register_dialect", "get_dialect", "list_dialects", "Sniffer",
"unregister_dialect", "__version__", "DictReader", "DictWriter",
"unix_dialect"]
class Dialect:
"""Describe a CSV dialect.
This must be subclassed (see csv.excel). Valid attributes are:
delimiter, quotechar, escapechar, doublequote, skipinitialspace,
lineterminator, quoting.
"""
_name = ""
_valid = False
# placeholders
delimiter = None
quotechar = None
escapechar = None
doublequote = None
skipinitialspace = None
lineterminator = None
quoting = None
def __init__(self):
if self.__class__ != Dialect:
self._valid = True
self._validate()
def _validate(self):
try:
_Dialect(self)
except TypeError as e:
# We do this for compatibility with py2.3
raise Error(str(e))
class excel(Dialect):
"""Describe the usual properties of Excel-generated CSV files."""
delimiter = ','
quotechar = '"'
doublequote = True
skipinitialspace = False
lineterminator = '\r\n'
quoting = QUOTE_MINIMAL
register_dialect("excel", excel)
class excel_tab(excel):
"""Describe the usual properties of Excel-generated TAB-delimited files."""
delimiter = '\t'
register_dialect("excel-tab", excel_tab)
class unix_dialect(Dialect):
"""Describe the usual properties of Unix-generated CSV files."""
delimiter = ','
quotechar = '"'
doublequote = True
skipinitialspace = False
lineterminator = '\n'
quoting = QUOTE_ALL
register_dialect("unix", unix_dialect)
class DictReader:
def __init__(self, f, fieldnames=None, restkey=None, restval=None,
dialect="excel", *args, **kwds):
self._fieldnames = fieldnames # list of keys for the dict
self.restkey = restkey # key to catch long rows
self.restval = restval # default value for short rows
self.reader = reader(f, dialect, *args, **kwds)
self.dialect = dialect
self.line_num = 0
def __iter__(self):
return self
@property
def fieldnames(self):
if self._fieldnames is None:
try:
self._fieldnames = next(self.reader)
except StopIteration:
pass
self.line_num = self.reader.line_num
return self._fieldnames
@fieldnames.setter
def fieldnames(self, value):
self._fieldnames = value
def __next__(self):
if self.line_num == 0:
# Used only for its side effect.
self.fieldnames
row = next(self.reader)
self.line_num = self.reader.line_num
# unlike the basic reader, we prefer not to return blanks,
# because we will typically wind up with a dict full of None
# values
while row == []:
row = next(self.reader)
d = dict(zip(self.fieldnames, row))
lf = len(self.fieldnames)
lr = len(row)
if lf < lr:
d[self.restkey] = row[lf:]
elif lf > lr:
for key in self.fieldnames[lr:]:
d[key] = self.restval
return d
class DictWriter:
def __init__(self, f, fieldnames, restval="", extrasaction="raise",
dialect="excel", *args, **kwds):
self.fieldnames = fieldnames # list of keys for the dict
self.restval = restval # for writing short dicts
if extrasaction.lower() not in ("raise", "ignore"):
raise ValueError("extrasaction (%s) must be 'raise' or 'ignore'"
% extrasaction)
self.extrasaction = extrasaction
self.writer = writer(f, dialect, *args, **kwds)
def writeheader(self):
header = dict(zip(self.fieldnames, self.fieldnames))
return self.writerow(header)
def _dict_to_list(self, rowdict):
if self.extrasaction == "raise":
wrong_fields = rowdict.keys() - self.fieldnames
if wrong_fields:
raise ValueError("dict contains fields not in fieldnames: "
+ ", ".join([repr(x) for x in wrong_fields]))
return (rowdict.get(key, self.restval) for key in self.fieldnames)
def writerow(self, rowdict):
return self.writer.writerow(self._dict_to_list(rowdict))
def writerows(self, rowdicts):
return self.writer.writerows(map(self._dict_to_list, rowdicts))
# Guard Sniffer's type checking against builds that exclude complex()
try:
complex
except NameError:
complex = float
class Sniffer:
'''
"Sniffs" the format of a CSV file (i.e. delimiter, quotechar)
Returns a Dialect object.
'''
def __init__(self):
# in case there is more than one possible delimiter
self.preferred = [',', '\t', ';', ' ', ':']
def sniff(self, sample, delimiters=None):
"""
Returns a dialect (or None) corresponding to the sample
"""
quotechar, doublequote, delimiter, skipinitialspace = \
self._guess_quote_and_delimiter(sample, delimiters)
if not delimiter:
delimiter, skipinitialspace = self._guess_delimiter(sample,
delimiters)
if not delimiter:
raise Error("Could not determine delimiter")
class dialect(Dialect):
_name = "sniffed"
lineterminator = '\r\n'
quoting = QUOTE_MINIMAL
# escapechar = ''
dialect.doublequote = doublequote
dialect.delimiter = delimiter
# _csv.reader won't accept a quotechar of ''
dialect.quotechar = quotechar or '"'
dialect.skipinitialspace = skipinitialspace
return dialect
def _guess_quote_and_delimiter(self, data, delimiters):
"""
Looks for text enclosed between two identical quotes
(the probable quotechar) which are preceded and followed
by the same character (the probable delimiter).
For example:
,'some text',
The quote with the most wins, same with the delimiter.
If there is no quotechar the delimiter can't be determined
this way.
"""
matches = []
for restr in (r'(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?P=delim)', # ,".*?",
r'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?P<delim>[^\w\n"\'])(?P<space> ?)', # ".*?",
r'(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?:$|\n)', # ,".*?"
r'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?:$|\n)'): # ".*?" (no delim, no space)
regexp = re.compile(restr, re.DOTALL | re.MULTILINE)
matches = regexp.findall(data)
if matches:
break
if not matches:
# (quotechar, doublequote, delimiter, skipinitialspace)
return ('', False, None, 0)
quotes = {}
delims = {}
spaces = 0
groupindex = regexp.groupindex
for m in matches:
n = groupindex['quote'] - 1
key = m[n]
if key:
quotes[key] = quotes.get(key, 0) + 1
try:
n = groupindex['delim'] - 1
key = m[n]
except KeyError:
continue
if key and (delimiters is None or key in delimiters):
delims[key] = delims.get(key, 0) + 1
try:
n = groupindex['space'] - 1
except KeyError:
continue
if m[n]:
spaces += 1
quotechar = max(quotes, key=quotes.get)
if delims:
delim = max(delims, key=delims.get)
skipinitialspace = delims[delim] == spaces
if delim == '\n': # most likely a file with a single column
delim = ''
else:
# there is *no* delimiter, it's a single column of quoted data
delim = ''
skipinitialspace = 0
# if we see an extra quote between delimiters, we've got a
# double quoted format
dq_regexp = re.compile(
r"((%(delim)s)|^)\W*%(quote)s[^%(delim)s\n]*%(quote)s[^%(delim)s\n]*%(quote)s\W*((%(delim)s)|$)" % \
{'delim':re.escape(delim), 'quote':quotechar}, re.MULTILINE)
if dq_regexp.search(data):
doublequote = True
else:
doublequote = False
return (quotechar, doublequote, delim, skipinitialspace)
def _guess_delimiter(self, data, delimiters):
"""
The delimiter /should/ occur the same number of times on
each row. However, due to malformed data, it may not. We don't want
an all or nothing approach, so we allow for small variations in this
number.
1) build a table of the frequency of each character on every line.
2) build a table of frequencies of this frequency (meta-frequency?),
e.g. 'x occurred 5 times in 10 rows, 6 times in 1000 rows,
7 times in 2 rows'
3) use the mode of the meta-frequency to determine the /expected/
frequency for that character
4) find out how often the character actually meets that goal
5) the character that best meets its goal is the delimiter
For performance reasons, the data is evaluated in chunks, so it can
try and evaluate the smallest portion of the data possible, evaluating
additional chunks as necessary.
"""
data = list(filter(None, data.split('\n')))
ascii = [chr(c) for c in range(127)] # 7-bit ASCII
# build frequency tables
chunkLength = min(10, len(data))
iteration = 0
charFrequency = {}
modes = {}
delims = {}
start, end = 0, chunkLength
while start < len(data):
iteration += 1
for line in data[start:end]:
for char in ascii:
metaFrequency = charFrequency.get(char, {})
# must count even if frequency is 0
freq = line.count(char)
# value is the mode
metaFrequency[freq] = metaFrequency.get(freq, 0) + 1
charFrequency[char] = metaFrequency
for char in charFrequency.keys():
items = list(charFrequency[char].items())
if len(items) == 1 and items[0][0] == 0:
continue
# get the mode of the frequencies
if len(items) > 1:
modes[char] = max(items, key=lambda x: x[1])
# adjust the mode - subtract the sum of all
# other frequencies
items.remove(modes[char])
modes[char] = (modes[char][0], modes[char][1]
- sum(item[1] for item in items))
else:
modes[char] = items[0]
# build a list of possible delimiters
modeList = modes.items()
total = float(min(chunkLength * iteration, len(data)))
# (rows of consistent data) / (number of rows) = 100%
consistency = 1.0
# minimum consistency threshold
threshold = 0.9
while len(delims) == 0 and consistency >= threshold:
for k, v in modeList:
if v[0] > 0 and v[1] > 0:
if ((v[1]/total) >= consistency and
(delimiters is None or k in delimiters)):
delims[k] = v
consistency -= 0.01
if len(delims) == 1:
delim = list(delims.keys())[0]
skipinitialspace = (data[0].count(delim) ==
data[0].count("%c " % delim))
return (delim, skipinitialspace)
# analyze another chunkLength lines
start = end
end += chunkLength
if not delims:
return ('', 0)
# if there's more than one, fall back to a 'preferred' list
if len(delims) > 1:
for d in self.preferred:
if d in delims.keys():
skipinitialspace = (data[0].count(d) ==
data[0].count("%c " % d))
return (d, skipinitialspace)
# nothing else indicates a preference, pick the character that
# dominates(?)
items = [(v,k) for (k,v) in delims.items()]
items.sort()
delim = items[-1][1]
skipinitialspace = (data[0].count(delim) ==
data[0].count("%c " % delim))
return (delim, skipinitialspace)
def has_header(self, sample):
# Creates a dictionary of types of data in each column. If any
# column is of a single type (say, integers), *except* for the first
# row, then the first row is presumed to be labels. If the type
# can't be determined, it is assumed to be a string in which case
# the length of the string is the determining factor: if all of the
# rows except for the first are the same length, it's a header.
# Finally, a 'vote' is taken at the end for each column, adding or
# subtracting from the likelihood of the first row being a header.
rdr = reader(StringIO(sample), self.sniff(sample))
header = next(rdr) # assume first row is header
columns = len(header)
columnTypes = {}
for i in range(columns): columnTypes[i] = None
checked = 0
for row in rdr:
# arbitrary number of rows to check, to keep it sane
if checked > 20:
break
checked += 1
if len(row) != columns:
continue # skip rows that have irregular number of columns
for col in list(columnTypes.keys()):
for thisType in [int, float, complex]:
try:
thisType(row[col])
break
except (ValueError, OverflowError):
pass
else:
# fallback to length of string
thisType = len(row[col])
if thisType != columnTypes[col]:
if columnTypes[col] is None: # add new column type
columnTypes[col] = thisType
else:
# type is inconsistent, remove column from
# consideration
del columnTypes[col]
# finally, compare results against first row and "vote"
# on whether it's a header
hasHeader = 0
for col, colType in columnTypes.items():
if type(colType) == type(0): # it's a length
if len(header[col]) != colType:
hasHeader += 1
else:
hasHeader -= 1
else: # attempt typecast
try:
colType(header[col])
except (ValueError, TypeError):
hasHeader += 1
else:
hasHeader -= 1
return hasHeader > 0
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